{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a Deep NN to predict Asset Price returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In practice, we need to explore variations of the design options outlined above because we can rarely be sure from the outset which network architecture best suits the data.\n",
    "\n",
    "In this section, we will explore various options to build a simple feedforward Neural Network to predict asset price returns for a one-day horizon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:48.879727Z",
     "start_time": "2020-06-21T18:08:48.877999Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:50.456974Z",
     "start_time": "2020-06-21T18:08:48.880955Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os, sys\n",
    "from ast import literal_eval as make_tuple\n",
    "from time import time\n",
    "from pathlib import Path\n",
    "from itertools import product\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "from scipy.stats import spearmanr\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:50.484830Z",
     "start_time": "2020-06-21T18:08:50.457987Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GPU\n"
     ]
    }
   ],
   "source": [
    "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpu_devices:\n",
    "    print('Using GPU')\n",
    "    tf.config.experimental.set_memory_growth(gpu_devices[0], True)\n",
    "else:\n",
    "    print('Using CPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:50.501779Z",
     "start_time": "2020-06-21T18:08:50.486181Z"
    }
   },
   "outputs": [],
   "source": [
    "sys.path.insert(1, os.path.join(sys.path[0], '..'))\n",
    "from utils import MultipleTimeSeriesCV, format_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:50.509383Z",
     "start_time": "2020-06-21T18:08:50.502654Z"
    }
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "sns.set_style('whitegrid')\n",
    "idx = pd.IndexSlice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:50.517197Z",
     "start_time": "2020-06-21T18:08:50.510329Z"
    }
   },
   "outputs": [],
   "source": [
    "DATA_STORE = '../data/assets.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T12:04:49.587124Z",
     "start_time": "2020-06-22T12:04:49.577688Z"
    }
   },
   "outputs": [],
   "source": [
    "results_path = Path('results')\n",
    "if not results_path.exists():\n",
    "    results_path.mkdir()\n",
    "    \n",
    "checkpoint_path = results_path / 'logs'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a stock return series to predict asset price moves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To develop our trading strategy, we use the daily stock returns for some 995 US stocks for the eight year period from 2010 to 2017, and the features developed in Chapter 12 that include volatility and momentum factors as well as lagged returns with cross-sectional and sectoral rankings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:51.538560Z",
     "start_time": "2020-06-21T18:08:50.527680Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_hdf('../12_gradient_boosting_machines/data.h5', 'model_data').dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:51.555255Z",
     "start_time": "2020-06-21T18:08:51.539453Z"
    }
   },
   "outputs": [],
   "source": [
    "outcomes = data.filter(like='fwd').columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:51.570274Z",
     "start_time": "2020-06-21T18:08:51.556209Z"
    }
   },
   "outputs": [],
   "source": [
    "lookahead = 1\n",
    "outcome= f'r{lookahead:02}_fwd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:52.531431Z",
     "start_time": "2020-06-21T18:08:51.571198Z"
    }
   },
   "outputs": [],
   "source": [
    "X_cv = data.loc[idx[:, :'2017'], :].drop(outcomes, axis=1)\n",
    "y_cv = data.loc[idx[:, :'2017'], outcome]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:52.594107Z",
     "start_time": "2020-06-21T18:08:52.532286Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "995"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_cv.index.get_level_values('symbol').unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:52.752655Z",
     "start_time": "2020-06-21T18:08:52.595138Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 1911344 entries, ('A', Timestamp('2010-04-06 00:00:00')) to ('ZION', Timestamp('2017-11-29 00:00:00'))\n",
      "Data columns (total 30 columns):\n",
      " #   Column           Non-Null Count    Dtype  \n",
      "---  ------           --------------    -----  \n",
      " 0   dollar_vol_rank  1911344 non-null  float64\n",
      " 1   rsi              1911344 non-null  float64\n",
      " 2   bb_high          1911344 non-null  float64\n",
      " 3   bb_low           1911344 non-null  float64\n",
      " 4   NATR             1911344 non-null  float64\n",
      " 5   ATR              1911344 non-null  float64\n",
      " 6   PPO              1911344 non-null  float64\n",
      " 7   MACD             1911344 non-null  float64\n",
      " 8   sector           1911344 non-null  int64  \n",
      " 9   r01              1911344 non-null  float64\n",
      " 10  r05              1911344 non-null  float64\n",
      " 11  r10              1911344 non-null  float64\n",
      " 12  r21              1911344 non-null  float64\n",
      " 13  r42              1911344 non-null  float64\n",
      " 14  r63              1911344 non-null  float64\n",
      " 15  r01dec           1911344 non-null  float64\n",
      " 16  r05dec           1911344 non-null  float64\n",
      " 17  r10dec           1911344 non-null  float64\n",
      " 18  r21dec           1911344 non-null  float64\n",
      " 19  r42dec           1911344 non-null  float64\n",
      " 20  r63dec           1911344 non-null  float64\n",
      " 21  r01q_sector      1911344 non-null  float64\n",
      " 22  r05q_sector      1911344 non-null  float64\n",
      " 23  r10q_sector      1911344 non-null  float64\n",
      " 24  r21q_sector      1911344 non-null  float64\n",
      " 25  r42q_sector      1911344 non-null  float64\n",
      " 26  r63q_sector      1911344 non-null  float64\n",
      " 27  year             1911344 non-null  int64  \n",
      " 28  month            1911344 non-null  int64  \n",
      " 29  weekday          1911344 non-null  int64  \n",
      "dtypes: float64(26), int64(4)\n",
      "memory usage: 444.9+ MB\n"
     ]
    }
   ],
   "source": [
    "X_cv.info(null_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automate model generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following `make_model` function illustrates how to flexibly define various architectural elements for the search process. The dense_layers argument defines both the depth and width of the network as a list of integers. We also use dropout for regularization, expressed as a float in the range [0, 1] to define the probability that a given unit will be excluded from a training iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:52.761403Z",
     "start_time": "2020-06-21T18:08:52.754021Z"
    }
   },
   "outputs": [],
   "source": [
    "def make_model(dense_layers, activation, dropout):\n",
    "    '''Creates a multi-layer perceptron model\n",
    "    \n",
    "    dense_layers: List of layer sizes; one number per layer\n",
    "    '''\n",
    "\n",
    "    model = Sequential()\n",
    "    for i, layer_size in enumerate(dense_layers, 1):\n",
    "        if i == 1:\n",
    "            model.add(Dense(layer_size, input_dim=X_cv.shape[1]))\n",
    "            model.add(Activation(activation))\n",
    "        else:\n",
    "            model.add(Dense(layer_size))\n",
    "            model.add(Activation(activation))\n",
    "    model.add(Dropout(dropout))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "    model.compile(loss='mean_squared_error',\n",
    "                  optimizer='Adam')\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-validate multiple configurations with TensorFlow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train-Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the data into a training set for cross-validation, and keep the last 12 months with data as holdout test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:52.770881Z",
     "start_time": "2020-06-21T18:08:52.762497Z"
    }
   },
   "outputs": [],
   "source": [
    "n_splits = 12\n",
    "train_period_length=21 * 12 * 4\n",
    "test_period_length=21 * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:52.785240Z",
     "start_time": "2020-06-21T18:08:52.772201Z"
    }
   },
   "outputs": [],
   "source": [
    "cv = MultipleTimeSeriesCV(n_splits=n_splits,\n",
    "                          train_period_length=train_period_length,\n",
    "                          test_period_length=test_period_length,\n",
    "                          lookahead=lookahead)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define CV Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we just need to define our Keras classifier using the make_model function, set cross-validation (see chapter 6 on The Machine Learning Process and following for the OneStepTimeSeriesSplit), and the parameters that we would like to explore. \n",
    "\n",
    "We pick several one- and two-layer configurations, relu and tanh activation functions, and different dropout rates. We could also try out different optimizers (but did not run this experiment to limit what is already a computationally intensive effort):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:52.793327Z",
     "start_time": "2020-06-21T18:08:52.786596Z"
    }
   },
   "outputs": [],
   "source": [
    "dense_layer_opts = [(16, 8), (32, 16), (32, 32), (64, 32)]\n",
    "activation_opts = ['tanh']\n",
    "dropout_opts = [0, .1, .2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:52.810201Z",
     "start_time": "2020-06-21T18:08:52.794375Z"
    }
   },
   "outputs": [],
   "source": [
    "param_grid = list(product(dense_layer_opts, activation_opts, dropout_opts))\n",
    "np.random.shuffle(param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:52.839581Z",
     "start_time": "2020-06-21T18:08:52.811266Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(param_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To trigger the parameter search, we instantiate a GridSearchCV object, define the fit_params that will be passed to the Keras model’s fit method, and provide the training data to the GridSearchCV fit method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-21T18:08:52.852132Z",
     "start_time": "2020-06-21T18:08:52.840794Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_train_valid_data(X, y, train_idx, test_idx):\n",
    "    x_train, y_train = X.iloc[train_idx, :], y.iloc[train_idx]\n",
    "    x_val, y_val = X.iloc[test_idx, :], y.iloc[test_idx]\n",
    "    return x_train, y_train, x_val, y_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T09:03:16.932114Z",
     "start_time": "2020-06-21T18:08:52.860680Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 32) tanh 0.1 64\n",
      "00:00:14 01 | 01 |  0.0063 |  0.0083\n",
      "00:00:27 01 | 02 |  0.0071 |  0.0114\n",
      "00:00:40 01 | 03 |  0.0063 |  0.0056\n",
      "00:00:52 01 | 04 |  0.0012 |  0.0083\n",
      "00:01:04 01 | 05 |  0.0172 |  0.0112\n",
      "00:01:17 01 | 06 |  0.0080 |  0.0115\n",
      "00:01:30 01 | 07 |  0.0166 |  0.0287\n",
      "00:01:42 01 | 08 |  0.0080 |  0.0186\n",
      "00:01:54 01 | 09 |  0.0175 |  0.0151\n",
      "00:02:07 01 | 10 | -0.0009 |  0.0072\n",
      "00:02:19 01 | 11 |  0.0124 |  0.0105\n",
      "00:02:31 01 | 12 |  0.0303 |  0.0291\n",
      "00:02:44 01 | 13 |  0.0160 |  0.0020\n",
      "00:02:56 01 | 14 |  0.0292 |  0.0273\n",
      "00:03:10 01 | 15 |  0.0198 |  0.0292\n",
      "00:03:23 01 | 16 |  0.0124 |  0.0136\n",
      "00:03:34 01 | 17 |  0.0202 |  0.0234\n",
      "00:03:46 01 | 18 |  0.0032 |  0.0038\n",
      "00:03:58 01 | 19 |  0.0181 |  0.0108\n",
      "00:04:10 01 | 20 |  0.0138 |  0.0149\n",
      "00:04:24 02 | 01 | -0.0218 | -0.0250\n",
      "00:04:36 02 | 02 | -0.0084 | -0.0208\n",
      "00:04:49 02 | 03 |  0.0173 |  0.0356\n",
      "00:05:01 02 | 04 |  0.0053 |  0.0141\n",
      "00:05:13 02 | 05 |  0.0156 |  0.0456\n",
      "00:05:26 02 | 06 |  0.0055 |  0.0068\n",
      "00:05:38 02 | 07 | -0.0123 | -0.0008\n",
      "00:05:51 02 | 08 | -0.0064 | -0.0018\n",
      "00:06:04 02 | 09 |  0.0039 |  0.0049\n",
      "00:06:17 02 | 10 |  0.0152 |  0.0206\n",
      "00:06:30 02 | 11 |  0.0056 |  0.0171\n",
      "00:06:42 02 | 12 | -0.0091 | -0.0209\n",
      "00:06:56 02 | 13 | -0.0124 | -0.0096\n",
      "00:07:08 02 | 14 | -0.0017 | -0.0282\n",
      "00:07:19 02 | 15 | -0.0058 | -0.0109\n",
      "00:07:30 02 | 16 | -0.0010 |  0.0044\n",
      "00:07:41 02 | 17 |  0.0055 |  0.0081\n",
      "00:07:52 02 | 18 | -0.0138 |  0.0037\n",
      "00:08:03 02 | 19 |  0.0141 |  0.0150\n",
      "00:08:16 02 | 20 |  0.0026 |  0.0053\n",
      "00:08:32 03 | 01 | -0.0202 | -0.0189\n",
      "00:08:43 03 | 02 | -0.0041 | -0.0055\n",
      "00:08:55 03 | 03 | -0.0022 | -0.0060\n",
      "00:09:05 03 | 04 | -0.0168 | -0.0411\n",
      "00:09:15 03 | 05 |  0.0165 |  0.0101\n",
      "00:09:26 03 | 06 | -0.0044 |  0.0079\n",
      "00:09:37 03 | 07 | -0.0016 | -0.0000\n",
      "00:09:48 03 | 08 | -0.0231 | -0.0299\n",
      "00:09:58 03 | 09 |  0.0123 |  0.0137\n",
      "00:10:09 03 | 10 |  0.0096 | -0.0020\n",
      "00:10:20 03 | 11 | -0.0105 | -0.0213\n",
      "00:10:31 03 | 12 | -0.0130 | -0.0170\n",
      "00:10:42 03 | 13 | -0.0117 | -0.0123\n",
      "00:10:53 03 | 14 | -0.0168 | -0.0112\n",
      "00:11:05 03 | 15 | -0.0043 |  0.0074\n",
      "00:11:16 03 | 16 | -0.0249 | -0.0429\n",
      "00:11:29 03 | 17 |  0.0138 |  0.0199\n",
      "00:11:40 03 | 18 |  0.0098 |  0.0016\n",
      "00:11:51 03 | 19 | -0.0109 | -0.0206\n",
      "00:12:02 03 | 20 | -0.0008 | -0.0005\n",
      "00:12:13 04 | 01 | -0.0038 | -0.0038\n",
      "00:12:24 04 | 02 | -0.0080 | -0.0185\n",
      "00:12:34 04 | 03 |  0.0138 | -0.0036\n",
      "00:12:45 04 | 04 |  0.0273 |  0.0215\n",
      "00:12:55 04 | 05 |  0.0152 | -0.0040\n",
      "00:13:05 04 | 06 |  0.0302 |  0.0107\n",
      "00:13:16 04 | 07 | -0.0077 | -0.0207\n",
      "00:13:26 04 | 08 |  0.0187 |  0.0001\n",
      "00:13:37 04 | 09 | -0.0146 | -0.0327\n",
      "00:13:47 04 | 10 | -0.0079 | -0.0142\n",
      "00:13:58 04 | 11 | -0.0185 | -0.0303\n",
      "00:14:08 04 | 12 | -0.0235 | -0.0241\n",
      "00:14:18 04 | 13 | -0.0002 | -0.0049\n",
      "00:14:28 04 | 14 |  0.0021 | -0.0005\n",
      "00:14:38 04 | 15 | -0.0207 | -0.0469\n",
      "00:14:49 04 | 16 |  0.0209 |  0.0030\n",
      "00:14:59 04 | 17 | -0.0497 | -0.0484\n",
      "00:15:09 04 | 18 | -0.0401 | -0.0422\n",
      "00:15:19 04 | 19 | -0.0295 | -0.0205\n",
      "00:15:29 04 | 20 | -0.0139 | -0.0488\n",
      "00:15:40 05 | 01 |  0.0124 |  0.0057\n",
      "00:15:50 05 | 02 |  0.0196 |  0.0267\n",
      "00:16:00 05 | 03 |  0.0020 | -0.0148\n",
      "00:16:10 05 | 04 |  0.0097 |  0.0150\n",
      "00:16:20 05 | 05 |  0.0148 |  0.0302\n",
      "00:16:30 05 | 06 |  0.0125 |  0.0268\n",
      "00:16:41 05 | 07 | -0.0063 |  0.0017\n",
      "00:16:51 05 | 08 | -0.0151 | -0.0240\n",
      "00:17:01 05 | 09 |  0.0246 |  0.0275\n",
      "00:17:11 05 | 10 | -0.0065 |  0.0030\n",
      "00:17:21 05 | 11 |  0.0029 |  0.0020\n",
      "00:17:32 05 | 12 |  0.0046 | -0.0178\n",
      "00:17:42 05 | 13 |  0.0031 | -0.0078\n",
      "00:17:52 05 | 14 |  0.0077 |  0.0286\n",
      "00:18:03 05 | 15 |  0.0007 |  0.0077\n",
      "00:18:13 05 | 16 |  0.0003 |  0.0022\n",
      "00:18:23 05 | 17 | -0.0215 | -0.0311\n",
      "00:18:33 05 | 18 | -0.0083 |  0.0083\n",
      "00:18:43 05 | 19 | -0.0088 | -0.0085\n",
      "00:18:53 05 | 20 |  0.0192 |  0.0196\n",
      "00:19:05 06 | 01 |  0.0037 |  0.0105\n",
      "00:19:15 06 | 02 |  0.0361 |  0.0266\n",
      "00:19:25 06 | 03 |  0.0184 |  0.0195\n",
      "00:19:35 06 | 04 | -0.0114 |  0.0039\n",
      "00:19:45 06 | 05 |  0.0365 |  0.0463\n",
      "00:19:56 06 | 06 |  0.0458 |  0.0233\n",
      "00:20:06 06 | 07 |  0.0533 |  0.0383\n",
      "00:20:17 06 | 08 |  0.0202 |  0.0070\n",
      "00:20:27 06 | 09 |  0.0311 |  0.0449\n",
      "00:20:38 06 | 10 |  0.0237 |  0.0246\n",
      "00:20:48 06 | 11 |  0.0182 |  0.0026\n",
      "00:20:58 06 | 12 |  0.0479 |  0.0342\n",
      "00:21:08 06 | 13 |  0.0156 |  0.0037\n",
      "00:21:19 06 | 14 |  0.0077 | -0.0053\n",
      "00:21:31 06 | 15 |  0.0198 |  0.0052\n",
      "00:21:43 06 | 16 |  0.0310 |  0.0368\n",
      "00:21:55 06 | 17 |  0.0420 |  0.0318\n",
      "00:22:05 06 | 18 |  0.0357 |  0.0337\n",
      "00:22:15 06 | 19 |  0.0174 |  0.0014\n",
      "00:22:25 06 | 20 |  0.0303 |  0.0329\n",
      "00:22:37 07 | 01 |  0.0204 |  0.0365\n",
      "00:22:47 07 | 02 |  0.0317 |  0.0367\n",
      "00:22:58 07 | 03 |  0.0070 |  0.0180\n",
      "00:23:09 07 | 04 | -0.0015 |  0.0315\n",
      "00:23:19 07 | 05 |  0.0351 |  0.0255\n",
      "00:23:30 07 | 06 |  0.0174 |  0.0413\n",
      "00:23:41 07 | 07 |  0.0285 |  0.0772\n",
      "00:23:52 07 | 08 | -0.0410 | -0.0335\n",
      "00:24:08 07 | 09 |  0.0326 |  0.0591\n",
      "00:24:24 07 | 10 |  0.0169 |  0.0443\n",
      "00:24:39 07 | 11 |  0.0178 |  0.0310\n",
      "00:24:53 07 | 12 | -0.0172 | -0.0038\n",
      "00:25:03 07 | 13 | -0.0018 |  0.0063\n",
      "00:25:13 07 | 14 |  0.0254 |  0.0465\n",
      "00:25:23 07 | 15 | -0.0018 | -0.0066\n",
      "00:25:34 07 | 16 |  0.0101 |  0.0020\n",
      "00:25:44 07 | 17 |  0.0218 |  0.0211\n",
      "00:26:05 07 | 18 | -0.0169 | -0.0052\n",
      "00:26:26 07 | 19 |  0.0117 |  0.0395\n",
      "00:26:48 07 | 20 |  0.0042 |  0.0064\n",
      "00:27:11 08 | 01 |  0.0061 |  0.0161\n",
      "00:27:32 08 | 02 |  0.0412 |  0.0459\n",
      "00:27:54 08 | 03 |  0.0247 |  0.0241\n",
      "00:28:16 08 | 04 |  0.0218 |  0.0293\n",
      "00:28:37 08 | 05 |  0.0023 |  0.0013\n",
      "00:28:59 08 | 06 | -0.0024 | -0.0003\n",
      "00:29:21 08 | 07 |  0.0168 |  0.0129\n",
      "00:29:43 08 | 08 |  0.0094 |  0.0183\n",
      "00:30:05 08 | 09 |  0.0143 |  0.0220\n",
      "00:30:23 08 | 10 |  0.0184 |  0.0105\n",
      "00:30:38 08 | 11 |  0.0068 |  0.0142\n",
      "00:30:54 08 | 12 |  0.0156 |  0.0306\n",
      "00:31:09 08 | 13 | -0.0021 | -0.0149\n",
      "00:31:25 08 | 14 |  0.0009 |  0.0019\n",
      "00:31:41 08 | 15 |  0.0010 |  0.0152\n",
      "00:31:56 08 | 16 | -0.0153 |  0.0279\n",
      "00:32:12 08 | 17 | -0.0034 |  0.0181\n",
      "00:32:27 08 | 18 |  0.0012 |  0.0000\n",
      "00:32:42 08 | 19 |  0.0025 |  0.0198\n",
      "00:32:57 08 | 20 |  0.0002 |  0.0288\n",
      "00:33:13 09 | 01 |  0.0094 |  0.0200\n",
      "00:33:29 09 | 02 | -0.0052 | -0.0267\n",
      "00:33:44 09 | 03 |  0.0135 |  0.0066\n",
      "00:34:01 09 | 04 |  0.0269 |  0.0360\n",
      "00:34:17 09 | 05 |  0.0213 |  0.0062\n",
      "00:34:34 09 | 06 |  0.0161 |  0.0091\n",
      "00:34:51 09 | 07 |  0.0201 |  0.0283\n",
      "00:35:09 09 | 08 |  0.0151 |  0.0095\n",
      "00:35:22 09 | 09 |  0.0083 | -0.0080\n",
      "00:35:35 09 | 10 |  0.0113 | -0.0129\n",
      "00:35:48 09 | 11 |  0.0151 |  0.0143\n",
      "00:36:04 09 | 12 |  0.0158 |  0.0051\n",
      "00:36:22 09 | 13 |  0.0064 | -0.0093\n",
      "00:36:39 09 | 14 |  0.0040 | -0.0085\n",
      "00:36:56 09 | 15 |  0.0089 | -0.0052\n",
      "00:37:14 09 | 16 |  0.0108 | -0.0138\n",
      "00:37:31 09 | 17 |  0.0078 | -0.0146\n",
      "00:37:48 09 | 18 |  0.0000 | -0.0127\n",
      "00:38:06 09 | 19 |  0.0166 |  0.0177\n",
      "00:38:24 09 | 20 |  0.0051 |  0.0007\n",
      "00:38:42 10 | 01 | -0.0439 | -0.0721\n",
      "00:38:59 10 | 02 |  0.0161 |  0.0144\n",
      "00:39:16 10 | 03 | -0.0127 | -0.0016\n",
      "00:39:34 10 | 04 |  0.0058 |  0.0028\n",
      "00:39:51 10 | 05 | -0.0083 | -0.0129\n",
      "00:40:08 10 | 06 |  0.0267 |  0.0254\n",
      "00:40:26 10 | 07 |  0.0192 |  0.0297\n",
      "00:40:43 10 | 08 | -0.0010 |  0.0077\n",
      "00:41:00 10 | 09 | -0.0105 | -0.0023\n",
      "00:41:18 10 | 10 |  0.0064 |  0.0035\n",
      "00:41:35 10 | 11 | -0.0159 | -0.0206\n",
      "00:41:53 10 | 12 |  0.0298 |  0.0168\n",
      "00:42:10 10 | 13 |  0.0068 |  0.0164\n",
      "00:42:27 10 | 14 |  0.0152 |  0.0284\n",
      "00:42:45 10 | 15 |  0.0173 |  0.0105\n",
      "00:43:02 10 | 16 |  0.0213 |  0.0177\n",
      "00:43:19 10 | 17 |  0.0257 |  0.0325\n",
      "00:43:33 10 | 18 |  0.0002 | -0.0019\n",
      "00:43:48 10 | 19 |  0.0262 |  0.0255\n",
      "00:44:02 10 | 20 |  0.0250 |  0.0226\n",
      "00:44:18 11 | 01 | -0.0187 | -0.0175\n",
      "00:44:32 11 | 02 | -0.0234 | -0.0265\n",
      "00:44:47 11 | 03 |  0.0034 |  0.0123\n",
      "00:45:01 11 | 04 |  0.0234 |  0.0311\n",
      "00:45:16 11 | 05 |  0.0047 |  0.0068\n",
      "00:45:30 11 | 06 |  0.0281 |  0.0293\n",
      "00:45:45 11 | 07 |  0.0209 |  0.0190\n",
      "00:45:60 11 | 08 |  0.0316 |  0.0272\n",
      "00:46:14 11 | 09 |  0.0118 |  0.0027\n",
      "00:46:29 11 | 10 |  0.0242 |  0.0256\n",
      "00:46:43 11 | 11 |  0.0256 |  0.0241\n",
      "00:46:58 11 | 12 |  0.0132 |  0.0070\n",
      "00:47:12 11 | 13 |  0.0123 |  0.0094\n",
      "00:47:27 11 | 14 |  0.0148 |  0.0190\n",
      "00:47:41 11 | 15 |  0.0070 | -0.0024\n",
      "00:47:56 11 | 16 |  0.0148 |  0.0192\n",
      "00:48:10 11 | 17 |  0.0026 | -0.0009\n",
      "00:48:24 11 | 18 |  0.0209 |  0.0166\n",
      "00:48:39 11 | 19 |  0.0061 | -0.0009\n",
      "00:48:53 11 | 20 | -0.0097 | -0.0050\n",
      "00:49:09 12 | 01 |  0.0098 |  0.0194\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:49:23 12 | 02 |  0.0026 |  0.0101\n",
      "00:49:37 12 | 03 | -0.0019 | -0.0319\n",
      "00:49:51 12 | 04 | -0.0035 |  0.0085\n",
      "00:50:06 12 | 05 | -0.0031 | -0.0010\n",
      "00:50:20 12 | 06 | -0.0047 |  0.0035\n",
      "00:50:34 12 | 07 |  0.0002 |  0.0080\n",
      "00:50:49 12 | 08 | -0.0017 |  0.0196\n",
      "00:51:03 12 | 09 |  0.0080 |  0.0130\n",
      "00:51:17 12 | 10 |  0.0118 |  0.0084\n",
      "00:51:32 12 | 11 | -0.0064 | -0.0127\n",
      "00:51:46 12 | 12 |  0.0187 |  0.0365\n",
      "00:52:00 12 | 13 |  0.0045 | -0.0084\n",
      "00:52:15 12 | 14 |  0.0105 |  0.0185\n",
      "00:52:29 12 | 15 |  0.0156 |  0.0220\n",
      "00:52:43 12 | 16 |  0.0208 |  0.0152\n",
      "00:52:58 12 | 17 |  0.0140 |  0.0269\n",
      "00:53:12 12 | 18 |  0.0082 | -0.0114\n",
      "00:53:27 12 | 19 |  0.0128 |  0.0052\n",
      "00:53:41 12 | 20 |  0.0164 |  0.0140\n",
      "(64, 32) tanh 0.1 256\n",
      "00:00:05 01 | 01 |  0.0090 |  0.0036\n",
      "00:00:10 01 | 02 |  0.0141 |  0.0241\n",
      "00:00:14 01 | 03 |  0.0001 | -0.0016\n",
      "00:00:18 01 | 04 |  0.0116 |  0.0044\n",
      "00:00:22 01 | 05 |  0.0038 |  0.0015\n",
      "00:00:27 01 | 06 |  0.0122 |  0.0187\n",
      "00:00:31 01 | 07 |  0.0082 |  0.0093\n",
      "00:00:35 01 | 08 |  0.0119 |  0.0020\n",
      "00:00:40 01 | 09 |  0.0164 |  0.0007\n",
      "00:00:44 01 | 10 |  0.0009 |  0.0026\n",
      "00:00:48 01 | 11 |  0.0120 |  0.0086\n",
      "00:00:52 01 | 12 |  0.0065 |  0.0081\n",
      "00:00:57 01 | 13 | -0.0006 |  0.0092\n",
      "00:01:01 01 | 14 |  0.0128 |  0.0082\n",
      "00:01:05 01 | 15 | -0.0046 | -0.0266\n",
      "00:01:10 01 | 16 |  0.0112 |  0.0153\n",
      "00:01:14 01 | 17 |  0.0052 | -0.0108\n",
      "00:01:18 01 | 18 |  0.0095 |  0.0162\n",
      "00:01:22 01 | 19 |  0.0061 |  0.0103\n",
      "00:01:27 01 | 20 | -0.0001 | -0.0032\n",
      "00:01:32 02 | 01 | -0.0256 | -0.0320\n",
      "00:01:36 02 | 02 |  0.0055 |  0.0069\n",
      "00:01:40 02 | 03 | -0.0056 | -0.0008\n",
      "00:01:45 02 | 04 |  0.0086 | -0.0103\n",
      "00:01:49 02 | 05 | -0.0164 | -0.0131\n",
      "00:01:53 02 | 06 |  0.0222 |  0.0339\n",
      "00:01:58 02 | 07 |  0.0116 | -0.0058\n",
      "00:02:02 02 | 08 |  0.0183 |  0.0059\n",
      "00:02:06 02 | 09 |  0.0126 |  0.0082\n",
      "00:02:11 02 | 10 |  0.0013 |  0.0026\n",
      "00:02:15 02 | 11 |  0.0119 |  0.0258\n",
      "00:02:19 02 | 12 | -0.0235 | -0.0217\n",
      "00:02:24 02 | 13 |  0.0191 |  0.0131\n",
      "00:02:28 02 | 14 |  0.0073 |  0.0060\n",
      "00:02:32 02 | 15 |  0.0065 |  0.0034\n",
      "00:02:37 02 | 16 |  0.0081 |  0.0188\n",
      "00:02:41 02 | 17 |  0.0092 |  0.0145\n",
      "00:02:45 02 | 18 |  0.0026 |  0.0123\n",
      "00:02:50 02 | 19 |  0.0078 | -0.0021\n",
      "00:02:53 02 | 20 |  0.0102 |  0.0018\n",
      "00:02:57 03 | 01 |  0.0039 |  0.0047\n",
      "00:02:60 03 | 02 | -0.0095 | -0.0051\n",
      "00:03:03 03 | 03 | -0.0106 | -0.0158\n",
      "00:03:05 03 | 04 | -0.0043 | -0.0089\n",
      "00:03:08 03 | 05 |  0.0146 |  0.0228\n",
      "00:03:11 03 | 06 | -0.0211 | -0.0272\n",
      "00:03:14 03 | 07 |  0.0283 |  0.0180\n",
      "00:03:17 03 | 08 |  0.0074 |  0.0236\n",
      "00:03:20 03 | 09 |  0.0028 | -0.0023\n",
      "00:03:23 03 | 10 |  0.0077 |  0.0021\n",
      "00:03:26 03 | 11 | -0.0225 | -0.0278\n",
      "00:03:29 03 | 12 |  0.0041 | -0.0012\n",
      "00:03:32 03 | 13 |  0.0022 | -0.0006\n",
      "00:03:35 03 | 14 | -0.0043 | -0.0147\n",
      "00:03:38 03 | 15 | -0.0127 | -0.0210\n",
      "00:03:41 03 | 16 |  0.0038 |  0.0040\n",
      "00:03:44 03 | 17 | -0.0028 | -0.0035\n",
      "00:03:47 03 | 18 | -0.0020 |  0.0042\n",
      "00:03:50 03 | 19 | -0.0156 | -0.0189\n",
      "00:03:53 03 | 20 |  0.0011 |  0.0040\n",
      "00:03:56 04 | 01 | -0.0321 | -0.0350\n",
      "00:03:59 04 | 02 |  0.0173 |  0.0068\n",
      "00:04:02 04 | 03 | -0.0287 | -0.0154\n",
      "00:04:05 04 | 04 | -0.0217 |  0.0081\n",
      "00:04:08 04 | 05 |  0.0298 |  0.0160\n",
      "00:04:11 04 | 06 |  0.0249 |  0.0184\n",
      "00:04:14 04 | 07 |  0.0361 |  0.0183\n",
      "00:04:17 04 | 08 | -0.0202 | -0.0218\n",
      "00:04:20 04 | 09 | -0.0022 | -0.0037\n",
      "00:04:23 04 | 10 |  0.0153 |  0.0153\n",
      "00:04:26 04 | 11 |  0.0215 |  0.0088\n",
      "00:04:29 04 | 12 |  0.0069 | -0.0067\n",
      "00:04:32 04 | 13 |  0.0219 |  0.0150\n",
      "00:04:35 04 | 14 |  0.0302 |  0.0198\n",
      "00:04:38 04 | 15 |  0.0225 |  0.0047\n",
      "00:04:41 04 | 16 |  0.0039 | -0.0062\n",
      "00:04:44 04 | 17 |  0.0245 |  0.0186\n",
      "00:04:47 04 | 18 |  0.0118 | -0.0058\n",
      "00:04:50 04 | 19 |  0.0067 |  0.0004\n",
      "00:04:52 04 | 20 |  0.0323 |  0.0128\n",
      "00:04:56 05 | 01 | -0.0109 | -0.0006\n",
      "00:04:59 05 | 02 |  0.0428 |  0.0243\n",
      "00:05:02 05 | 03 |  0.0163 |  0.0015\n",
      "00:05:05 05 | 04 | -0.0138 | -0.0409\n",
      "00:05:08 05 | 05 |  0.0291 |  0.0173\n",
      "00:05:11 05 | 06 |  0.0043 | -0.0146\n",
      "00:05:14 05 | 07 |  0.0073 | -0.0008\n",
      "00:05:17 05 | 08 |  0.0065 |  0.0172\n",
      "00:05:20 05 | 09 |  0.0024 | -0.0077\n",
      "00:05:23 05 | 10 | -0.0058 | -0.0233\n",
      "00:05:26 05 | 11 |  0.0099 |  0.0237\n",
      "00:05:29 05 | 12 | -0.0021 |  0.0181\n",
      "00:05:32 05 | 13 |  0.0150 |  0.0217\n",
      "00:05:35 05 | 14 |  0.0006 |  0.0182\n",
      "00:05:38 05 | 15 |  0.0005 |  0.0050\n",
      "00:05:41 05 | 16 |  0.0029 |  0.0190\n",
      "00:05:44 05 | 17 | -0.0089 | -0.0058\n",
      "00:05:47 05 | 18 | -0.0116 | -0.0121\n",
      "00:05:50 05 | 19 | -0.0107 |  0.0068\n",
      "00:05:53 05 | 20 |  0.0050 |  0.0144\n",
      "00:05:56 06 | 01 | -0.0126 | -0.0178\n",
      "00:05:59 06 | 02 |  0.0426 |  0.0861\n",
      "00:06:02 06 | 03 |  0.0149 |  0.0288\n",
      "00:06:05 06 | 04 |  0.0183 |  0.0016\n",
      "00:06:08 06 | 05 |  0.0358 |  0.0242\n",
      "00:06:11 06 | 06 |  0.0270 |  0.0249\n",
      "00:06:14 06 | 07 |  0.0482 |  0.0412\n",
      "00:06:17 06 | 08 |  0.0346 |  0.0634\n",
      "00:06:20 06 | 09 |  0.0255 |  0.0268\n",
      "00:06:23 06 | 10 |  0.0266 |  0.0287\n",
      "00:06:26 06 | 11 |  0.0370 |  0.0073\n",
      "00:06:29 06 | 12 |  0.0170 |  0.0169\n",
      "00:06:32 06 | 13 |  0.0214 |  0.0102\n",
      "00:06:35 06 | 14 |  0.0232 |  0.0157\n",
      "00:06:38 06 | 15 |  0.0195 |  0.0109\n",
      "00:06:41 06 | 16 |  0.0104 |  0.0084\n",
      "00:06:44 06 | 17 |  0.0038 |  0.0104\n",
      "00:06:47 06 | 18 |  0.0159 |  0.0321\n",
      "00:06:50 06 | 19 |  0.0266 |  0.0357\n",
      "00:06:53 06 | 20 |  0.0113 |  0.0176\n",
      "00:06:56 07 | 01 |  0.0492 |  0.0554\n",
      "00:06:59 07 | 02 | -0.0299 | -0.0554\n",
      "00:07:02 07 | 03 |  0.0162 |  0.0383\n",
      "00:07:05 07 | 04 |  0.0196 |  0.0081\n",
      "00:07:08 07 | 05 |  0.0068 |  0.0096\n",
      "00:07:11 07 | 06 |  0.0197 |  0.0172\n",
      "00:07:14 07 | 07 |  0.0069 |  0.0224\n",
      "00:07:17 07 | 08 |  0.0226 |  0.0305\n",
      "00:07:20 07 | 09 |  0.0125 |  0.0145\n",
      "00:07:23 07 | 10 |  0.0193 |  0.0404\n",
      "00:07:26 07 | 11 | -0.0144 |  0.0206\n",
      "00:07:29 07 | 12 |  0.0033 |  0.0179\n",
      "00:07:32 07 | 13 |  0.0158 |  0.0278\n",
      "00:07:35 07 | 14 |  0.0171 |  0.0156\n",
      "00:07:38 07 | 15 |  0.0177 |  0.0278\n",
      "00:07:41 07 | 16 |  0.0127 | -0.0062\n",
      "00:07:44 07 | 17 |  0.0233 |  0.0163\n",
      "00:07:47 07 | 18 |  0.0271 |  0.0092\n",
      "00:07:50 07 | 19 |  0.0161 |  0.0019\n",
      "00:07:53 07 | 20 |  0.0103 |  0.0022\n",
      "00:07:56 08 | 01 |  0.0053 |  0.0092\n",
      "00:07:59 08 | 02 |  0.0082 |  0.0173\n",
      "00:08:02 08 | 03 |  0.0056 |  0.0142\n",
      "00:08:05 08 | 04 |  0.0194 |  0.0368\n",
      "00:08:08 08 | 05 |  0.0178 |  0.0070\n",
      "00:08:11 08 | 06 |  0.0093 | -0.0045\n",
      "00:08:14 08 | 07 |  0.0117 |  0.0120\n",
      "00:08:17 08 | 08 |  0.0214 |  0.0181\n",
      "00:08:20 08 | 09 |  0.0285 |  0.0146\n",
      "00:08:23 08 | 10 |  0.0211 |  0.0380\n",
      "00:08:26 08 | 11 |  0.0180 |  0.0033\n",
      "00:08:28 08 | 12 |  0.0179 |  0.0033\n",
      "00:08:31 08 | 13 |  0.0138 | -0.0006\n",
      "00:08:34 08 | 14 |  0.0146 |  0.0205\n",
      "00:08:37 08 | 15 |  0.0183 |  0.0265\n",
      "00:08:40 08 | 16 |  0.0260 |  0.0307\n",
      "00:08:43 08 | 17 |  0.0161 |  0.0095\n",
      "00:08:46 08 | 18 |  0.0102 |  0.0127\n",
      "00:08:49 08 | 19 |  0.0159 |  0.0289\n",
      "00:08:52 08 | 20 | -0.0028 | -0.0007\n",
      "00:08:56 09 | 01 | -0.0068 | -0.0142\n",
      "00:08:59 09 | 02 |  0.0137 |  0.0176\n",
      "00:09:02 09 | 03 |  0.0332 |  0.0199\n",
      "00:09:05 09 | 04 |  0.0021 | -0.0028\n",
      "00:09:08 09 | 05 | -0.0135 | -0.0276\n",
      "00:09:11 09 | 06 |  0.0193 |  0.0129\n",
      "00:09:14 09 | 07 |  0.0284 |  0.0175\n",
      "00:09:17 09 | 08 |  0.0148 |  0.0135\n",
      "00:09:20 09 | 09 |  0.0267 |  0.0274\n",
      "00:09:23 09 | 10 | -0.0074 | -0.0129\n",
      "00:09:26 09 | 11 |  0.0208 |  0.0300\n",
      "00:09:28 09 | 12 |  0.0202 |  0.0193\n",
      "00:09:31 09 | 13 |  0.0109 |  0.0077\n",
      "00:09:34 09 | 14 |  0.0068 | -0.0193\n",
      "00:09:37 09 | 15 |  0.0139 |  0.0010\n",
      "00:09:40 09 | 16 |  0.0025 | -0.0092\n",
      "00:09:43 09 | 17 |  0.0081 | -0.0083\n",
      "00:09:46 09 | 18 |  0.0014 | -0.0087\n",
      "00:09:49 09 | 19 |  0.0094 |  0.0109\n",
      "00:09:52 09 | 20 | -0.0023 | -0.0040\n",
      "00:09:56 10 | 01 |  0.0116 |  0.0057\n",
      "00:09:59 10 | 02 |  0.0556 |  0.0689\n",
      "00:10:02 10 | 03 |  0.0077 | -0.0041\n",
      "00:10:05 10 | 04 |  0.0331 |  0.0482\n",
      "00:10:08 10 | 05 | -0.0060 | -0.0120\n",
      "00:10:11 10 | 06 | -0.0247 | -0.0118\n",
      "00:10:14 10 | 07 |  0.0265 |  0.0299\n",
      "00:10:17 10 | 08 |  0.0018 |  0.0160\n",
      "00:10:20 10 | 09 | -0.0312 | -0.0437\n",
      "00:10:23 10 | 10 | -0.0143 | -0.0192\n",
      "00:10:26 10 | 11 | -0.0124 | -0.0018\n",
      "00:10:29 10 | 12 | -0.0441 | -0.0485\n",
      "00:10:32 10 | 13 |  0.0036 |  0.0120\n",
      "00:10:35 10 | 14 | -0.0184 | -0.0088\n",
      "00:10:37 10 | 15 |  0.0137 |  0.0158\n",
      "00:10:40 10 | 16 |  0.0125 |  0.0174\n",
      "00:10:43 10 | 17 |  0.0009 |  0.0192\n",
      "00:10:46 10 | 18 |  0.0018 |  0.0061\n",
      "00:10:49 10 | 19 | -0.0009 |  0.0017\n",
      "00:10:52 10 | 20 | -0.0214 | -0.0133\n",
      "00:10:56 11 | 01 |  0.0189 |  0.0159\n",
      "00:10:59 11 | 02 |  0.0181 |  0.0059\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:11:02 11 | 03 |  0.0184 |  0.0230\n",
      "00:11:05 11 | 04 |  0.0347 |  0.0367\n",
      "00:11:08 11 | 05 |  0.0192 |  0.0219\n",
      "00:11:11 11 | 06 |  0.0090 |  0.0158\n",
      "00:11:14 11 | 07 |  0.0064 |  0.0039\n",
      "00:11:17 11 | 08 | -0.0045 | -0.0079\n",
      "00:11:20 11 | 09 |  0.0182 |  0.0136\n",
      "00:11:23 11 | 10 |  0.0226 |  0.0287\n",
      "00:11:26 11 | 11 |  0.0084 |  0.0046\n",
      "00:11:29 11 | 12 |  0.0284 |  0.0218\n",
      "00:11:32 11 | 13 | -0.0066 | -0.0122\n",
      "00:11:35 11 | 14 |  0.0150 |  0.0085\n",
      "00:11:38 11 | 15 | -0.0105 | -0.0110\n",
      "00:11:40 11 | 16 |  0.0071 |  0.0095\n",
      "00:11:43 11 | 17 | -0.0054 | -0.0046\n",
      "00:11:46 11 | 18 | -0.0128 |  0.0008\n",
      "00:11:49 11 | 19 | -0.0078 | -0.0018\n",
      "00:11:52 11 | 20 | -0.0170 | -0.0236\n",
      "00:11:56 12 | 01 |  0.0122 |  0.0273\n",
      "00:11:59 12 | 02 |  0.0116 |  0.0263\n",
      "00:12:02 12 | 03 |  0.0148 |  0.0319\n",
      "00:12:05 12 | 04 | -0.0067 | -0.0285\n",
      "00:12:08 12 | 05 | -0.0139 | -0.0406\n",
      "00:12:11 12 | 06 |  0.0092 | -0.0147\n",
      "00:12:14 12 | 07 |  0.0134 |  0.0006\n",
      "00:12:17 12 | 08 |  0.0073 |  0.0280\n",
      "00:12:20 12 | 09 | -0.0047 | -0.0193\n",
      "00:12:23 12 | 10 | -0.0021 | -0.0218\n",
      "00:12:26 12 | 11 |  0.0147 | -0.0042\n",
      "00:12:29 12 | 12 | -0.0125 | -0.0137\n",
      "00:12:32 12 | 13 | -0.0088 | -0.0186\n",
      "00:12:35 12 | 14 | -0.0107 | -0.0147\n",
      "00:12:37 12 | 15 |  0.0038 | -0.0255\n",
      "00:12:40 12 | 16 |  0.0091 |  0.0077\n",
      "00:12:43 12 | 17 |  0.0131 |  0.0168\n",
      "00:12:46 12 | 18 |  0.0145 |  0.0181\n",
      "00:12:49 12 | 19 |  0.0012 |  0.0025\n",
      "00:12:52 12 | 20 |  0.0047 |  0.0039\n",
      "(64, 32) tanh 0 64\n",
      "00:00:11 01 | 01 |  0.0076 |  0.0084\n",
      "00:00:20 01 | 02 |  0.0170 |  0.0070\n",
      "00:00:30 01 | 03 |  0.0084 |  0.0035\n",
      "00:00:39 01 | 04 |  0.0185 |  0.0128\n",
      "00:00:49 01 | 05 |  0.0048 | -0.0089\n",
      "00:00:59 01 | 06 |  0.0117 |  0.0090\n",
      "00:01:08 01 | 07 | -0.0055 | -0.0092\n",
      "00:01:18 01 | 08 |  0.0056 |  0.0068\n",
      "00:01:27 01 | 09 |  0.0166 |  0.0240\n",
      "00:01:37 01 | 10 |  0.0117 |  0.0146\n",
      "00:01:47 01 | 11 |  0.0305 |  0.0430\n",
      "00:01:56 01 | 12 |  0.0177 |  0.0172\n",
      "00:02:06 01 | 13 | -0.0038 |  0.0098\n",
      "00:02:15 01 | 14 | -0.0032 | -0.0030\n",
      "00:02:25 01 | 15 |  0.0159 |  0.0125\n",
      "00:02:35 01 | 16 |  0.0085 |  0.0071\n",
      "00:02:44 01 | 17 |  0.0105 |  0.0073\n",
      "00:02:54 01 | 18 |  0.0003 |  0.0115\n",
      "00:03:03 01 | 19 |  0.0056 |  0.0039\n",
      "00:03:13 01 | 20 |  0.0087 |  0.0104\n",
      "00:03:23 02 | 01 |  0.0336 |  0.0294\n",
      "00:03:33 02 | 02 | -0.0124 | -0.0011\n",
      "00:03:42 02 | 03 |  0.0109 |  0.0167\n",
      "00:03:52 02 | 04 | -0.0022 |  0.0075\n",
      "00:04:02 02 | 05 | -0.0008 |  0.0092\n",
      "00:04:11 02 | 06 | -0.0026 | -0.0173\n",
      "00:04:21 02 | 07 |  0.0098 |  0.0182\n",
      "00:04:30 02 | 08 |  0.0107 |  0.0225\n",
      "00:04:40 02 | 09 | -0.0083 | -0.0083\n",
      "00:04:50 02 | 10 |  0.0024 |  0.0164\n",
      "00:04:59 02 | 11 |  0.0074 | -0.0052\n",
      "00:05:09 02 | 12 |  0.0173 |  0.0277\n",
      "00:05:18 02 | 13 |  0.0145 |  0.0086\n",
      "00:05:28 02 | 14 |  0.0095 |  0.0069\n",
      "00:05:37 02 | 15 | -0.0036 | -0.0209\n",
      "00:05:47 02 | 16 | -0.0027 | -0.0149\n",
      "00:05:57 02 | 17 |  0.0102 |  0.0130\n",
      "00:06:06 02 | 18 |  0.0088 |  0.0148\n",
      "00:06:16 02 | 19 |  0.0073 |  0.0189\n",
      "00:06:25 02 | 20 |  0.0097 |  0.0015\n",
      "00:06:36 03 | 01 |  0.0297 |  0.0305\n",
      "00:06:45 03 | 02 |  0.0033 | -0.0175\n",
      "00:06:55 03 | 03 |  0.0184 |  0.0292\n",
      "00:07:05 03 | 04 |  0.0150 |  0.0267\n",
      "00:07:14 03 | 05 |  0.0045 | -0.0006\n",
      "00:07:24 03 | 06 | -0.0032 | -0.0166\n",
      "00:07:33 03 | 07 | -0.0164 | -0.0184\n",
      "00:07:43 03 | 08 | -0.0038 |  0.0050\n",
      "00:07:53 03 | 09 | -0.0034 | -0.0060\n",
      "00:08:02 03 | 10 | -0.0088 | -0.0117\n",
      "00:08:12 03 | 11 |  0.0032 | -0.0007\n",
      "00:08:21 03 | 12 | -0.0303 | -0.0477\n",
      "00:08:31 03 | 13 |  0.0069 | -0.0090\n",
      "00:08:41 03 | 14 |  0.0059 |  0.0187\n",
      "00:08:50 03 | 15 |  0.0022 |  0.0132\n",
      "00:09:00 03 | 16 |  0.0104 |  0.0200\n",
      "00:09:10 03 | 17 |  0.0189 |  0.0285\n",
      "00:09:19 03 | 18 |  0.0079 |  0.0057\n",
      "00:09:29 03 | 19 | -0.0032 | -0.0271\n",
      "00:09:38 03 | 20 |  0.0051 |  0.0086\n",
      "00:09:49 04 | 01 |  0.0013 | -0.0029\n",
      "00:09:58 04 | 02 | -0.0107 | -0.0074\n",
      "00:10:08 04 | 03 |  0.0052 | -0.0016\n",
      "00:10:18 04 | 04 | -0.0107 |  0.0013\n",
      "00:10:27 04 | 05 | -0.0097 | -0.0355\n",
      "00:10:37 04 | 06 | -0.0068 |  0.0021\n",
      "00:10:47 04 | 07 | -0.0382 | -0.0280\n",
      "00:10:56 04 | 08 |  0.0048 |  0.0001\n",
      "00:11:06 04 | 09 |  0.0286 |  0.0356\n",
      "00:11:16 04 | 10 | -0.0344 | -0.0218\n",
      "00:11:25 04 | 11 | -0.0067 |  0.0029\n",
      "00:11:35 04 | 12 |  0.0055 | -0.0198\n",
      "00:11:45 04 | 13 | -0.0198 | -0.0286\n",
      "00:11:54 04 | 14 | -0.0263 | -0.0390\n",
      "00:12:04 04 | 15 | -0.0199 | -0.0291\n",
      "00:12:13 04 | 16 | -0.0098 |  0.0014\n",
      "00:12:23 04 | 17 | -0.0067 |  0.0025\n",
      "00:12:33 04 | 18 |  0.0094 | -0.0203\n",
      "00:12:42 04 | 19 | -0.0170 |  0.0115\n",
      "00:12:52 04 | 20 | -0.0280 | -0.0197\n",
      "00:13:02 05 | 01 | -0.0033 |  0.0047\n",
      "00:13:12 05 | 02 |  0.0186 |  0.0118\n",
      "00:13:23 05 | 03 |  0.0285 |  0.0411\n",
      "00:13:33 05 | 04 | -0.0182 | -0.0243\n",
      "00:13:42 05 | 05 | -0.0170 | -0.0151\n",
      "00:13:53 05 | 06 | -0.0079 | -0.0084\n",
      "00:14:02 05 | 07 |  0.0101 | -0.0030\n",
      "00:14:12 05 | 08 |  0.0137 |  0.0175\n",
      "00:14:22 05 | 09 |  0.0155 |  0.0191\n",
      "00:14:32 05 | 10 |  0.0061 |  0.0021\n",
      "00:14:42 05 | 11 | -0.0104 | -0.0062\n",
      "00:14:52 05 | 12 |  0.0124 |  0.0082\n",
      "00:15:05 05 | 13 | -0.0121 | -0.0091\n",
      "00:15:34 05 | 14 |  0.0024 |  0.0223\n",
      "00:16:06 05 | 15 | -0.0048 | -0.0012\n",
      "00:16:33 05 | 16 |  0.0026 | -0.0053\n",
      "00:16:58 05 | 17 |  0.0016 | -0.0055\n",
      "00:17:30 05 | 18 |  0.0177 |  0.0172\n",
      "00:17:57 05 | 19 |  0.0085 |  0.0136\n",
      "00:18:29 05 | 20 | -0.0046 |  0.0103\n",
      "00:18:47 06 | 01 |  0.0152 |  0.0311\n",
      "00:18:57 06 | 02 |  0.0145 |  0.0364\n",
      "00:19:07 06 | 03 |  0.0072 |  0.0110\n",
      "00:19:17 06 | 04 |  0.0443 |  0.0435\n",
      "00:19:27 06 | 05 |  0.0340 |  0.0152\n",
      "00:19:37 06 | 06 |  0.0391 |  0.0242\n",
      "00:19:47 06 | 07 |  0.0209 |  0.0192\n",
      "00:19:57 06 | 08 |  0.0467 |  0.0307\n",
      "00:20:07 06 | 09 |  0.0037 | -0.0032\n",
      "00:20:17 06 | 10 |  0.0326 |  0.0172\n",
      "00:20:27 06 | 11 |  0.0312 |  0.0204\n",
      "00:20:37 06 | 12 |  0.0093 | -0.0104\n",
      "00:20:47 06 | 13 |  0.0195 |  0.0117\n",
      "00:20:57 06 | 14 |  0.0333 |  0.0187\n",
      "00:21:14 06 | 15 | -0.0067 | -0.0139\n",
      "00:21:40 06 | 16 | -0.0080 | -0.0016\n",
      "00:22:13 06 | 17 |  0.0198 |  0.0131\n",
      "00:22:39 06 | 18 | -0.0045 |  0.0041\n",
      "00:23:07 06 | 19 |  0.0271 |  0.0024\n",
      "00:23:38 06 | 20 |  0.0221 |  0.0110\n",
      "00:24:07 07 | 01 |  0.0006 |  0.0023\n",
      "00:24:26 07 | 02 |  0.0008 |  0.0142\n",
      "00:24:43 07 | 03 |  0.0280 |  0.0366\n",
      "00:25:10 07 | 04 |  0.0289 |  0.0208\n",
      "00:25:39 07 | 05 |  0.0218 |  0.0214\n",
      "00:26:10 07 | 06 |  0.0362 |  0.0653\n",
      "00:26:44 07 | 07 |  0.0501 |  0.0484\n",
      "00:27:17 07 | 08 |  0.0097 |  0.0446\n",
      "00:27:51 07 | 09 |  0.0348 |  0.0572\n",
      "00:28:24 07 | 10 |  0.0030 |  0.0086\n",
      "00:28:58 07 | 11 |  0.0119 |  0.0374\n",
      "00:29:30 07 | 12 |  0.0006 |  0.0088\n",
      "00:30:02 07 | 13 | -0.0029 |  0.0127\n",
      "00:30:34 07 | 14 | -0.0114 |  0.0039\n",
      "00:31:07 07 | 15 | -0.0242 | -0.0184\n",
      "00:31:40 07 | 16 | -0.0021 |  0.0200\n",
      "00:31:54 07 | 17 | -0.0111 | -0.0089\n",
      "00:32:04 07 | 18 |  0.0007 | -0.0011\n",
      "00:32:15 07 | 19 |  0.0070 |  0.0070\n",
      "00:32:26 07 | 20 |  0.0092 |  0.0051\n",
      "00:32:37 08 | 01 | -0.0159 |  0.0105\n",
      "00:32:47 08 | 02 |  0.0088 |  0.0265\n",
      "00:32:57 08 | 03 |  0.0394 |  0.0587\n",
      "00:33:08 08 | 04 |  0.0052 |  0.0138\n",
      "00:33:19 08 | 05 |  0.0321 |  0.0279\n",
      "00:33:30 08 | 06 |  0.0417 |  0.0364\n",
      "00:33:40 08 | 07 |  0.0278 |  0.0362\n",
      "00:34:01 08 | 08 |  0.0330 |  0.0174\n",
      "00:34:33 08 | 09 |  0.0209 |  0.0272\n",
      "00:35:05 08 | 10 |  0.0055 |  0.0080\n",
      "00:35:37 08 | 11 |  0.0241 |  0.0386\n",
      "00:36:08 08 | 12 |  0.0150 |  0.0049\n",
      "00:36:44 08 | 13 |  0.0150 |  0.0251\n",
      "00:37:18 08 | 14 |  0.0109 |  0.0091\n",
      "00:37:59 08 | 15 |  0.0231 |  0.0240\n",
      "00:38:43 08 | 16 |  0.0285 |  0.0413\n",
      "00:39:28 08 | 17 |  0.0321 |  0.0338\n",
      "00:40:09 08 | 18 |  0.0336 |  0.0346\n",
      "00:40:53 08 | 19 |  0.0269 |  0.0194\n",
      "00:41:31 08 | 20 |  0.0036 | -0.0082\n",
      "00:42:07 09 | 01 |  0.0059 |  0.0034\n",
      "00:42:46 09 | 02 |  0.0077 | -0.0114\n",
      "00:43:02 09 | 03 |  0.0242 |  0.0342\n",
      "00:43:14 09 | 04 |  0.0102 |  0.0109\n",
      "00:43:26 09 | 05 |  0.0306 |  0.0227\n",
      "00:43:43 09 | 06 |  0.0049 |  0.0126\n",
      "00:44:03 09 | 07 |  0.0065 |  0.0009\n",
      "00:44:22 09 | 08 |  0.0002 | -0.0154\n",
      "00:44:41 09 | 09 |  0.0024 | -0.0008\n",
      "00:45:01 09 | 10 | -0.0049 | -0.0203\n",
      "00:45:20 09 | 11 | -0.0013 | -0.0201\n",
      "00:45:37 09 | 12 | -0.0025 | -0.0026\n",
      "00:45:55 09 | 13 | -0.0046 | -0.0039\n",
      "00:46:13 09 | 14 | -0.0080 | -0.0039\n",
      "00:46:30 09 | 15 |  0.0048 | -0.0002\n",
      "00:46:41 09 | 16 |  0.0069 | -0.0037\n",
      "00:46:53 09 | 17 |  0.0022 |  0.0078\n",
      "00:47:04 09 | 18 | -0.0138 |  0.0015\n",
      "00:47:16 09 | 19 | -0.0012 | -0.0110\n",
      "00:47:27 09 | 20 |  0.0027 | -0.0015\n",
      "00:47:39 10 | 01 | -0.0464 | -0.0521\n",
      "00:47:51 10 | 02 | -0.0292 | -0.0538\n",
      "00:48:03 10 | 03 |  0.0471 |  0.0685\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:48:14 10 | 04 | -0.0316 | -0.0370\n",
      "00:48:25 10 | 05 | -0.0402 | -0.0606\n",
      "00:48:38 10 | 06 | -0.0417 | -0.0526\n",
      "00:48:55 10 | 07 | -0.0295 | -0.0391\n",
      "00:49:13 10 | 08 |  0.0577 |  0.0689\n",
      "00:49:29 10 | 09 |  0.0550 |  0.0649\n",
      "00:49:47 10 | 10 | -0.0459 | -0.0559\n",
      "00:50:03 10 | 11 |  0.0070 | -0.0013\n",
      "00:50:21 10 | 12 | -0.0145 | -0.0247\n",
      "00:50:35 10 | 13 |  0.0241 |  0.0232\n",
      "00:50:47 10 | 14 | -0.0445 | -0.0617\n",
      "00:50:59 10 | 15 | -0.0428 | -0.0677\n",
      "00:51:13 10 | 16 |  0.0406 |  0.0396\n",
      "00:51:31 10 | 17 | -0.0428 | -0.0455\n",
      "00:51:48 10 | 18 | -0.0113 | -0.0015\n",
      "00:52:04 10 | 19 |  0.0485 |  0.0529\n",
      "00:52:20 10 | 20 | -0.0404 | -0.0496\n",
      "00:52:38 11 | 01 |  0.0097 |  0.0050\n",
      "00:52:54 11 | 02 | -0.0094 | -0.0163\n",
      "00:53:10 11 | 03 |  0.0235 |  0.0263\n",
      "00:53:27 11 | 04 |  0.0260 |  0.0153\n",
      "00:53:41 11 | 05 |  0.0222 |  0.0186\n",
      "00:53:55 11 | 06 |  0.0240 |  0.0274\n",
      "00:54:10 11 | 07 |  0.0103 | -0.0002\n",
      "00:54:24 11 | 08 |  0.0167 |  0.0106\n",
      "00:54:38 11 | 09 |  0.0215 |  0.0238\n",
      "00:54:53 11 | 10 |  0.0122 |  0.0107\n",
      "00:55:08 11 | 11 | -0.0013 |  0.0021\n",
      "00:55:22 11 | 12 |  0.0160 |  0.0235\n",
      "00:55:36 11 | 13 |  0.0150 |  0.0106\n",
      "00:55:51 11 | 14 | -0.0102 | -0.0128\n",
      "00:56:05 11 | 15 |  0.0091 |  0.0262\n",
      "00:56:20 11 | 16 |  0.0087 |  0.0073\n",
      "00:56:35 11 | 17 | -0.0002 | -0.0085\n",
      "00:56:49 11 | 18 |  0.0013 | -0.0031\n",
      "00:57:04 11 | 19 | -0.0041 | -0.0036\n",
      "00:57:18 11 | 20 |  0.0085 |  0.0017\n",
      "00:57:34 12 | 01 |  0.0202 |  0.0242\n",
      "00:57:48 12 | 02 |  0.0029 | -0.0219\n",
      "00:58:00 12 | 03 |  0.0034 | -0.0040\n",
      "00:58:14 12 | 04 | -0.0056 | -0.0009\n",
      "00:58:27 12 | 05 |  0.0045 |  0.0169\n",
      "00:58:39 12 | 06 |  0.0204 |  0.0280\n",
      "00:58:52 12 | 07 |  0.0114 |  0.0086\n",
      "00:59:04 12 | 08 |  0.0337 |  0.0327\n",
      "00:59:17 12 | 09 |  0.0240 |  0.0187\n",
      "00:59:30 12 | 10 | -0.0018 |  0.0009\n",
      "00:59:44 12 | 11 |  0.0173 |  0.0321\n",
      "00:59:57 12 | 12 |  0.0229 |  0.0355\n",
      "01:00:09 12 | 13 |  0.0181 |  0.0096\n",
      "01:00:22 12 | 14 |  0.0187 |  0.0138\n",
      "01:00:34 12 | 15 |  0.0164 |  0.0276\n",
      "01:00:47 12 | 16 |  0.0316 |  0.0282\n",
      "01:00:59 12 | 17 |  0.0275 |  0.0347\n",
      "01:01:16 12 | 18 |  0.0351 |  0.0483\n",
      "01:01:30 12 | 19 |  0.0296 |  0.0254\n",
      "01:01:43 12 | 20 |  0.0384 |  0.0357\n",
      "(64, 32) tanh 0 256\n",
      "00:00:05 01 | 01 |  0.0148 |  0.0129\n",
      "00:00:09 01 | 02 |  0.0003 | -0.0038\n",
      "00:00:12 01 | 03 |  0.0160 |  0.0267\n",
      "00:00:16 01 | 04 |  0.0341 |  0.0562\n",
      "00:00:20 01 | 05 |  0.0153 |  0.0118\n",
      "00:00:24 01 | 06 |  0.0198 |  0.0047\n",
      "00:00:27 01 | 07 | -0.0062 | -0.0009\n",
      "00:00:31 01 | 08 | -0.0068 | -0.0116\n",
      "00:00:35 01 | 09 |  0.0097 | -0.0016\n",
      "00:00:38 01 | 10 |  0.0074 |  0.0083\n",
      "00:00:42 01 | 11 | -0.0054 |  0.0015\n",
      "00:00:46 01 | 12 | -0.0023 | -0.0093\n",
      "00:00:50 01 | 13 | -0.0090 | -0.0027\n",
      "00:00:53 01 | 14 |  0.0053 |  0.0090\n",
      "00:00:57 01 | 15 |  0.0097 |  0.0232\n",
      "00:01:01 01 | 16 |  0.0005 |  0.0093\n",
      "00:01:05 01 | 17 |  0.0088 |  0.0038\n",
      "00:01:11 01 | 18 |  0.0097 |  0.0179\n",
      "00:01:17 01 | 19 |  0.0044 | -0.0033\n",
      "00:01:21 01 | 20 |  0.0131 |  0.0049\n",
      "00:01:26 02 | 01 |  0.0008 |  0.0037\n",
      "00:01:30 02 | 02 |  0.0161 |  0.0055\n",
      "00:01:33 02 | 03 |  0.0231 |  0.0206\n",
      "00:01:37 02 | 04 | -0.0185 | -0.0183\n",
      "00:01:41 02 | 05 |  0.0024 |  0.0119\n",
      "00:01:45 02 | 06 |  0.0196 |  0.0121\n",
      "00:01:48 02 | 07 |  0.0158 |  0.0197\n",
      "00:01:52 02 | 08 | -0.0015 | -0.0162\n",
      "00:01:56 02 | 09 |  0.0071 |  0.0083\n",
      "00:01:60 02 | 10 | -0.0031 | -0.0014\n",
      "00:02:04 02 | 11 | -0.0165 | -0.0084\n",
      "00:02:07 02 | 12 | -0.0010 |  0.0078\n",
      "00:02:11 02 | 13 | -0.0015 |  0.0020\n",
      "00:02:15 02 | 14 |  0.0070 |  0.0117\n",
      "00:02:19 02 | 15 |  0.0071 |  0.0096\n",
      "00:02:22 02 | 16 |  0.0097 |  0.0028\n",
      "00:02:26 02 | 17 |  0.0107 |  0.0184\n",
      "00:02:30 02 | 18 |  0.0118 |  0.0216\n",
      "00:02:34 02 | 19 |  0.0070 |  0.0204\n",
      "00:02:37 02 | 20 |  0.0052 |  0.0043\n",
      "00:02:42 03 | 01 | -0.0137 | -0.0225\n",
      "00:02:47 03 | 02 | -0.0066 | -0.0001\n",
      "00:02:53 03 | 03 | -0.0297 | -0.0243\n",
      "00:02:59 03 | 04 |  0.0127 |  0.0005\n",
      "00:03:03 03 | 05 | -0.0255 | -0.0543\n",
      "00:03:07 03 | 06 | -0.0180 | -0.0179\n",
      "00:03:11 03 | 07 | -0.0053 | -0.0105\n",
      "00:03:14 03 | 08 | -0.0103 | -0.0126\n",
      "00:03:18 03 | 09 | -0.0022 | -0.0036\n",
      "00:03:22 03 | 10 | -0.0207 | -0.0208\n",
      "00:03:26 03 | 11 |  0.0011 | -0.0017\n",
      "00:03:29 03 | 12 | -0.0107 | -0.0004\n",
      "00:03:33 03 | 13 | -0.0237 | -0.0129\n",
      "00:03:37 03 | 14 | -0.0099 | -0.0189\n",
      "00:03:41 03 | 15 | -0.0061 | -0.0027\n",
      "00:03:44 03 | 16 |  0.0063 |  0.0151\n",
      "00:03:48 03 | 17 | -0.0116 | -0.0018\n",
      "00:03:52 03 | 18 | -0.0151 | -0.0028\n",
      "00:03:56 03 | 19 | -0.0048 | -0.0000\n",
      "00:03:59 03 | 20 | -0.0150 | -0.0096\n",
      "00:04:04 04 | 01 |  0.0003 | -0.0053\n",
      "00:04:08 04 | 02 |  0.0084 |  0.0073\n",
      "00:04:12 04 | 03 |  0.0556 |  0.0371\n",
      "00:04:15 04 | 04 |  0.0143 |  0.0074\n",
      "00:04:19 04 | 05 |  0.0227 | -0.0057\n",
      "00:04:23 04 | 06 |  0.0076 |  0.0117\n",
      "00:04:28 04 | 07 |  0.0085 |  0.0065\n",
      "00:04:34 04 | 08 | -0.0304 | -0.0273\n",
      "00:04:40 04 | 09 | -0.0091 | -0.0265\n",
      "00:04:45 04 | 10 |  0.0359 |  0.0260\n",
      "00:04:49 04 | 11 |  0.0029 | -0.0054\n",
      "00:04:53 04 | 12 |  0.0122 | -0.0018\n",
      "00:04:57 04 | 13 | -0.0065 | -0.0172\n",
      "00:05:01 04 | 14 |  0.0123 |  0.0126\n",
      "00:05:04 04 | 15 |  0.0134 |  0.0100\n",
      "00:05:08 04 | 16 | -0.0034 | -0.0288\n",
      "00:05:12 04 | 17 |  0.0184 |  0.0063\n",
      "00:05:16 04 | 18 |  0.0003 |  0.0043\n",
      "00:05:19 04 | 19 |  0.0209 | -0.0041\n",
      "00:05:23 04 | 20 |  0.0212 |  0.0156\n",
      "00:05:28 05 | 01 |  0.0003 |  0.0072\n",
      "00:05:32 05 | 02 |  0.0226 |  0.0115\n",
      "00:05:35 05 | 03 |  0.0036 |  0.0114\n",
      "00:05:39 05 | 04 |  0.0100 |  0.0290\n",
      "00:05:43 05 | 05 | -0.0095 | -0.0092\n",
      "00:05:47 05 | 06 | -0.0167 | -0.0035\n",
      "00:05:50 05 | 07 | -0.0254 | -0.0169\n",
      "00:05:54 05 | 08 |  0.0081 |  0.0204\n",
      "00:05:58 05 | 09 | -0.0068 |  0.0027\n",
      "00:06:01 05 | 10 |  0.0133 |  0.0166\n",
      "00:06:05 05 | 11 | -0.0059 | -0.0008\n",
      "00:06:10 05 | 12 | -0.0073 | -0.0048\n",
      "00:06:16 05 | 13 | -0.0169 | -0.0123\n",
      "00:06:22 05 | 14 | -0.0087 | -0.0233\n",
      "00:06:28 05 | 15 |  0.0101 |  0.0150\n",
      "00:06:33 05 | 16 | -0.0170 | -0.0292\n",
      "00:06:37 05 | 17 | -0.0032 |  0.0042\n",
      "00:06:40 05 | 18 | -0.0154 | -0.0180\n",
      "00:06:44 05 | 19 |  0.0038 |  0.0021\n",
      "00:06:48 05 | 20 | -0.0001 |  0.0038\n",
      "00:06:52 06 | 01 |  0.0097 |  0.0225\n",
      "00:06:56 06 | 02 |  0.0056 |  0.0064\n",
      "00:07:00 06 | 03 |  0.0004 | -0.0193\n",
      "00:07:04 06 | 04 |  0.0336 |  0.0368\n",
      "00:07:08 06 | 05 |  0.0460 |  0.0382\n",
      "00:07:11 06 | 06 |  0.0274 |  0.0245\n",
      "00:07:15 06 | 07 |  0.0204 |  0.0199\n",
      "00:07:19 06 | 08 |  0.0157 |  0.0244\n",
      "00:07:23 06 | 09 |  0.0437 |  0.0425\n",
      "00:07:31 06 | 10 |  0.0494 |  0.0354\n",
      "00:07:40 06 | 11 |  0.0147 |  0.0110\n",
      "00:07:49 06 | 12 |  0.0230 |  0.0292\n",
      "00:07:58 06 | 13 |  0.0214 |  0.0286\n",
      "00:08:06 06 | 14 |  0.0265 |  0.0467\n",
      "00:08:16 06 | 15 | -0.0025 | -0.0266\n",
      "00:08:24 06 | 16 |  0.0170 | -0.0037\n",
      "00:08:32 06 | 17 |  0.0251 |  0.0279\n",
      "00:08:41 06 | 18 |  0.0223 |  0.0207\n",
      "00:08:50 06 | 19 |  0.0164 | -0.0147\n",
      "00:09:01 06 | 20 |  0.0288 |  0.0141\n",
      "00:09:11 07 | 01 | -0.0271 | -0.0331\n",
      "00:09:19 07 | 02 |  0.0089 |  0.0070\n",
      "00:09:32 07 | 03 |  0.0393 |  0.0473\n",
      "00:09:41 07 | 04 |  0.0216 |  0.0315\n",
      "00:09:50 07 | 05 | -0.0233 | -0.0285\n",
      "00:10:02 07 | 06 |  0.0227 |  0.0210\n",
      "00:10:07 07 | 07 |  0.0249 |  0.0349\n",
      "00:10:18 07 | 08 |  0.0038 | -0.0169\n",
      "00:10:26 07 | 09 |  0.0018 |  0.0194\n",
      "00:10:36 07 | 10 |  0.0007 |  0.0095\n",
      "00:10:47 07 | 11 |  0.0135 |  0.0224\n",
      "00:10:56 07 | 12 |  0.0169 |  0.0239\n",
      "00:11:08 07 | 13 |  0.0129 |  0.0253\n",
      "00:11:18 07 | 14 |  0.0016 | -0.0011\n",
      "00:11:26 07 | 15 |  0.0291 |  0.0249\n",
      "00:11:38 07 | 16 |  0.0252 |  0.0062\n",
      "00:11:46 07 | 17 |  0.0071 | -0.0004\n",
      "00:11:55 07 | 18 |  0.0093 | -0.0093\n",
      "00:12:06 07 | 19 |  0.0206 | -0.0137\n",
      "00:12:13 07 | 20 |  0.0164 |  0.0213\n",
      "00:12:24 08 | 01 |  0.0140 |  0.0034\n",
      "00:12:34 08 | 02 | -0.0122 | -0.0105\n",
      "00:12:42 08 | 03 |  0.0034 |  0.0305\n",
      "00:12:53 08 | 04 |  0.0230 |  0.0130\n",
      "00:13:00 08 | 05 |  0.0018 |  0.0184\n",
      "00:13:10 08 | 06 |  0.0263 |  0.0140\n",
      "00:13:20 08 | 07 |  0.0196 |  0.0038\n",
      "00:13:28 08 | 08 |  0.0304 |  0.0163\n",
      "00:13:39 08 | 09 |  0.0322 |  0.0222\n",
      "00:13:47 08 | 10 |  0.0229 |  0.0153\n",
      "00:13:55 08 | 11 |  0.0141 |  0.0174\n",
      "00:14:07 08 | 12 |  0.0265 |  0.0345\n",
      "00:14:14 08 | 13 |  0.0032 | -0.0008\n",
      "00:14:24 08 | 14 |  0.0044 | -0.0020\n",
      "00:14:34 08 | 15 |  0.0265 |  0.0424\n",
      "00:14:43 08 | 16 |  0.0231 |  0.0339\n",
      "00:14:55 08 | 17 |  0.0130 |  0.0075\n",
      "00:14:59 08 | 18 |  0.0149 |  0.0223\n",
      "00:15:02 08 | 19 |  0.0207 |  0.0189\n",
      "00:15:06 08 | 20 |  0.0130 |  0.0180\n",
      "00:15:10 09 | 01 | -0.0008 | -0.0133\n",
      "00:15:13 09 | 02 |  0.0368 |  0.0378\n",
      "00:15:16 09 | 03 | -0.0138 | -0.0327\n",
      "00:15:19 09 | 04 | -0.0150 | -0.0325\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:15:23 09 | 05 |  0.0238 |  0.0179\n",
      "00:15:31 09 | 06 |  0.0126 |  0.0187\n",
      "00:15:40 09 | 07 |  0.0205 |  0.0179\n",
      "00:15:47 09 | 08 |  0.0057 |  0.0058\n",
      "00:15:56 09 | 09 |  0.0055 |  0.0010\n",
      "00:16:04 09 | 10 |  0.0154 |  0.0166\n",
      "00:16:13 09 | 11 | -0.0026 | -0.0041\n",
      "00:16:20 09 | 12 |  0.0137 |  0.0074\n",
      "00:16:30 09 | 13 |  0.0039 |  0.0023\n",
      "00:16:41 09 | 14 | -0.0028 | -0.0118\n",
      "00:16:47 09 | 15 |  0.0086 | -0.0036\n",
      "00:16:57 09 | 16 | -0.0021 | -0.0032\n",
      "00:17:05 09 | 17 |  0.0067 | -0.0007\n",
      "00:17:17 09 | 18 |  0.0010 | -0.0024\n",
      "00:17:21 09 | 19 |  0.0023 | -0.0085\n",
      "00:17:33 09 | 20 |  0.0081 | -0.0016\n",
      "00:17:42 10 | 01 | -0.0418 | -0.0392\n",
      "00:17:51 10 | 02 | -0.0191 | -0.0144\n",
      "00:18:03 10 | 03 |  0.0185 |  0.0175\n",
      "00:18:11 10 | 04 | -0.0260 | -0.0138\n",
      "00:18:21 10 | 05 | -0.0362 | -0.0272\n",
      "00:18:31 10 | 06 | -0.0420 | -0.0575\n",
      "00:18:39 10 | 07 | -0.0345 | -0.0349\n",
      "00:18:51 10 | 08 | -0.0408 | -0.0395\n",
      "00:18:58 10 | 09 | -0.0444 | -0.0313\n",
      "00:19:08 10 | 10 | -0.0177 | -0.0326\n",
      "00:19:19 10 | 11 | -0.0254 | -0.0232\n",
      "00:19:28 10 | 12 | -0.0244 | -0.0361\n",
      "00:19:38 10 | 13 | -0.0029 | -0.0019\n",
      "00:19:49 10 | 14 | -0.0264 | -0.0362\n",
      "00:19:57 10 | 15 | -0.0199 | -0.0261\n",
      "00:20:09 10 | 16 | -0.0236 | -0.0209\n",
      "00:20:19 10 | 17 | -0.0231 | -0.0309\n",
      "00:20:30 10 | 18 | -0.0301 | -0.0413\n",
      "00:20:43 10 | 19 | -0.0167 | -0.0365\n",
      "00:20:56 10 | 20 | -0.0355 | -0.0380\n",
      "00:21:09 11 | 01 |  0.0057 |  0.0019\n",
      "00:21:21 11 | 02 |  0.0103 |  0.0069\n",
      "00:21:33 11 | 03 |  0.0235 |  0.0255\n",
      "00:21:48 11 | 04 |  0.0094 |  0.0066\n",
      "00:21:60 11 | 05 |  0.0258 |  0.0205\n",
      "00:22:12 11 | 06 |  0.0019 |  0.0190\n",
      "00:22:23 11 | 07 |  0.0153 |  0.0204\n",
      "00:22:37 11 | 08 | -0.0031 |  0.0008\n",
      "00:22:49 11 | 09 |  0.0092 |  0.0164\n",
      "00:23:00 11 | 10 |  0.0203 |  0.0253\n",
      "00:23:13 11 | 11 |  0.0213 |  0.0277\n",
      "00:23:28 11 | 12 |  0.0044 |  0.0049\n",
      "00:23:39 11 | 13 |  0.0169 |  0.0029\n",
      "00:23:48 11 | 14 | -0.0061 | -0.0106\n",
      "00:24:01 11 | 15 | -0.0024 | -0.0132\n",
      "00:24:09 11 | 16 |  0.0083 |  0.0092\n",
      "00:24:19 11 | 17 | -0.0005 | -0.0013\n",
      "00:24:32 11 | 18 |  0.0063 |  0.0041\n",
      "00:24:40 11 | 19 |  0.0019 | -0.0074\n",
      "00:24:46 11 | 20 |  0.0031 | -0.0062\n",
      "00:24:52 12 | 01 | -0.0071 |  0.0214\n",
      "00:25:04 12 | 02 | -0.0009 | -0.0138\n",
      "00:25:09 12 | 03 | -0.0008 | -0.0065\n",
      "00:25:21 12 | 04 | -0.0044 | -0.0309\n",
      "00:25:26 12 | 05 |  0.0039 |  0.0259\n",
      "00:25:38 12 | 06 |  0.0157 |  0.0012\n",
      "00:25:43 12 | 07 |  0.0078 | -0.0060\n",
      "00:25:55 12 | 08 |  0.0089 |  0.0135\n",
      "00:26:01 12 | 09 | -0.0084 | -0.0073\n",
      "00:26:12 12 | 10 |  0.0132 |  0.0060\n",
      "00:26:21 12 | 11 |  0.0067 |  0.0109\n",
      "00:26:30 12 | 12 |  0.0028 |  0.0113\n",
      "00:26:40 12 | 13 |  0.0038 | -0.0021\n",
      "00:26:48 12 | 14 |  0.0009 | -0.0122\n",
      "00:26:60 12 | 15 | -0.0083 | -0.0120\n",
      "00:27:08 12 | 16 | -0.0002 | -0.0145\n",
      "00:27:18 12 | 17 | -0.0046 | -0.0091\n",
      "00:27:31 12 | 18 | -0.0001 | -0.0492\n",
      "00:27:40 12 | 19 |  0.0118 | -0.0028\n",
      "00:27:51 12 | 20 |  0.0001 |  0.0099\n",
      "(16, 8) tanh 0 64\n",
      "00:00:37 01 | 01 |  0.0029 | -0.0009\n",
      "00:01:11 01 | 02 |  0.0245 |  0.0348\n",
      "00:01:45 01 | 03 |  0.0143 | -0.0027\n",
      "00:02:19 01 | 04 | -0.0106 | -0.0142\n",
      "00:02:54 01 | 05 | -0.0069 |  0.0067\n",
      "00:03:28 01 | 06 | -0.0007 |  0.0003\n",
      "00:03:38 01 | 07 |  0.0127 |  0.0162\n",
      "00:03:48 01 | 08 |  0.0008 | -0.0014\n",
      "00:03:59 01 | 09 | -0.0008 | -0.0047\n",
      "00:04:09 01 | 10 | -0.0113 | -0.0081\n",
      "00:04:20 01 | 11 |  0.0021 |  0.0118\n",
      "00:04:31 01 | 12 | -0.0117 | -0.0123\n",
      "00:04:44 01 | 13 | -0.0076 | -0.0173\n",
      "00:04:56 01 | 14 | -0.0026 | -0.0103\n",
      "00:05:08 01 | 15 | -0.0038 | -0.0121\n",
      "00:05:20 01 | 16 | -0.0073 | -0.0147\n",
      "00:05:33 01 | 17 | -0.0149 | -0.0198\n",
      "00:05:45 01 | 18 |  0.0064 |  0.0117\n",
      "00:05:59 01 | 19 | -0.0033 | -0.0075\n",
      "00:06:11 01 | 20 | -0.0009 | -0.0106\n",
      "00:06:23 02 | 01 | -0.0017 | -0.0158\n",
      "00:06:35 02 | 02 |  0.0092 |  0.0163\n",
      "00:06:46 02 | 03 |  0.0024 |  0.0195\n",
      "00:06:57 02 | 04 |  0.0227 |  0.0189\n",
      "00:07:08 02 | 05 |  0.0071 | -0.0046\n",
      "00:07:19 02 | 06 | -0.0057 | -0.0036\n",
      "00:07:29 02 | 07 |  0.0171 |  0.0140\n",
      "00:07:40 02 | 08 | -0.0012 | -0.0015\n",
      "00:07:54 02 | 09 | -0.0025 | -0.0053\n",
      "00:08:07 02 | 10 |  0.0026 | -0.0095\n",
      "00:08:20 02 | 11 |  0.0031 |  0.0069\n",
      "00:08:32 02 | 12 | -0.0004 | -0.0096\n",
      "00:08:45 02 | 13 |  0.0146 |  0.0124\n",
      "00:08:57 02 | 14 |  0.0013 | -0.0012\n",
      "00:09:10 02 | 15 |  0.0011 |  0.0079\n",
      "00:09:22 02 | 16 | -0.0096 |  0.0021\n",
      "00:09:34 02 | 17 | -0.0039 |  0.0291\n",
      "00:09:47 02 | 18 | -0.0077 |  0.0117\n",
      "00:09:59 02 | 19 |  0.0022 |  0.0015\n",
      "00:10:10 02 | 20 |  0.0043 | -0.0070\n",
      "00:10:23 03 | 01 |  0.0072 |  0.0140\n",
      "00:10:36 03 | 02 | -0.0022 | -0.0152\n",
      "00:10:49 03 | 03 |  0.0056 |  0.0192\n",
      "00:11:01 03 | 04 | -0.0036 | -0.0120\n",
      "00:11:14 03 | 05 | -0.0095 | -0.0343\n",
      "00:11:27 03 | 06 | -0.0094 | -0.0063\n",
      "00:11:38 03 | 07 | -0.0053 | -0.0210\n",
      "00:11:50 03 | 08 |  0.0215 |  0.0225\n",
      "00:12:02 03 | 09 |  0.0062 | -0.0046\n",
      "00:12:14 03 | 10 | -0.0259 | -0.0494\n",
      "00:12:27 03 | 11 | -0.0161 | -0.0078\n",
      "00:12:38 03 | 12 | -0.0098 | -0.0145\n",
      "00:12:49 03 | 13 | -0.0092 | -0.0091\n",
      "00:12:60 03 | 14 | -0.0041 | -0.0235\n",
      "00:13:33 03 | 15 |  0.0140 |  0.0293\n",
      "00:14:15 03 | 16 |  0.0082 |  0.0288\n",
      "00:14:57 03 | 17 | -0.0112 | -0.0196\n",
      "00:15:39 03 | 18 |  0.0164 |  0.0123\n",
      "00:16:22 03 | 19 |  0.0089 |  0.0081\n",
      "00:17:03 03 | 20 | -0.0032 |  0.0003\n",
      "00:17:44 04 | 01 |  0.0122 | -0.0047\n",
      "00:18:25 04 | 02 |  0.0208 |  0.0049\n",
      "00:19:08 04 | 03 | -0.0093 | -0.0147\n",
      "00:19:50 04 | 04 | -0.0239 | -0.0363\n",
      "00:20:34 04 | 05 | -0.0273 | -0.0518\n",
      "00:21:20 04 | 06 | -0.0039 | -0.0018\n",
      "00:22:09 04 | 07 | -0.0065 | -0.0174\n",
      "00:22:56 04 | 08 | -0.0405 | -0.0403\n",
      "00:23:45 04 | 09 | -0.0394 | -0.0409\n",
      "00:24:32 04 | 10 | -0.0285 | -0.0487\n",
      "00:25:23 04 | 11 | -0.0015 |  0.0038\n",
      "00:26:11 04 | 12 | -0.0201 | -0.0268\n",
      "00:26:57 04 | 13 | -0.0292 | -0.0147\n",
      "00:27:42 04 | 14 |  0.0066 |  0.0263\n",
      "00:28:32 04 | 15 | -0.0235 | -0.0086\n",
      "00:29:19 04 | 16 | -0.0146 | -0.0076\n",
      "00:30:07 04 | 17 | -0.0079 | -0.0050\n",
      "00:30:57 04 | 18 |  0.0066 |  0.0113\n",
      "00:31:44 04 | 19 | -0.0181 |  0.0031\n",
      "00:32:26 04 | 20 | -0.0031 |  0.0190\n",
      "00:33:45 05 | 01 | -0.0092 | -0.0108\n",
      "00:34:43 05 | 02 | -0.0168 | -0.0247\n",
      "00:35:42 05 | 03 |  0.0010 | -0.0021\n",
      "00:36:42 05 | 04 | -0.0049 | -0.0044\n",
      "00:37:37 05 | 05 |  0.0002 | -0.0001\n",
      "00:38:28 05 | 06 |  0.0258 |  0.0138\n",
      "00:39:19 05 | 07 |  0.0233 |  0.0132\n",
      "00:40:10 05 | 08 |  0.0048 |  0.0041\n",
      "00:40:59 05 | 09 |  0.0208 |  0.0185\n",
      "00:41:51 05 | 10 |  0.0115 |  0.0083\n",
      "00:42:43 05 | 11 | -0.0020 | -0.0057\n",
      "00:43:35 05 | 12 |  0.0143 |  0.0183\n",
      "00:44:10 05 | 13 |  0.0006 |  0.0009\n",
      "00:44:22 05 | 14 |  0.0206 |  0.0193\n",
      "00:44:33 05 | 15 |  0.0222 |  0.0055\n",
      "00:44:44 05 | 16 |  0.0233 |  0.0221\n",
      "00:44:55 05 | 17 |  0.0170 |  0.0103\n",
      "00:45:05 05 | 18 |  0.0242 |  0.0232\n",
      "00:45:16 05 | 19 |  0.0121 | -0.0031\n",
      "00:45:26 05 | 20 |  0.0101 |  0.0046\n",
      "00:45:37 06 | 01 | -0.0263 | -0.0065\n",
      "00:45:47 06 | 02 |  0.0242 |  0.0129\n",
      "00:45:58 06 | 03 |  0.0205 |  0.0425\n",
      "00:46:08 06 | 04 |  0.0143 |  0.0180\n",
      "00:46:18 06 | 05 |  0.0074 | -0.0100\n",
      "00:46:29 06 | 06 |  0.0360 |  0.0359\n",
      "00:46:39 06 | 07 |  0.0458 |  0.0473\n",
      "00:46:49 06 | 08 |  0.0274 |  0.0065\n",
      "00:46:60 06 | 09 |  0.0132 |  0.0118\n",
      "00:47:10 06 | 10 | -0.0021 | -0.0218\n",
      "00:47:20 06 | 11 |  0.0272 |  0.0380\n",
      "00:47:31 06 | 12 |  0.0323 |  0.0291\n",
      "00:47:41 06 | 13 |  0.0396 |  0.0612\n",
      "00:47:51 06 | 14 |  0.0195 |  0.0352\n",
      "00:48:01 06 | 15 |  0.0311 |  0.0236\n",
      "00:48:12 06 | 16 |  0.0381 |  0.0343\n",
      "00:48:22 06 | 17 |  0.0290 |  0.0299\n",
      "00:48:32 06 | 18 |  0.0292 |  0.0103\n",
      "00:48:42 06 | 19 |  0.0326 |  0.0290\n",
      "00:48:53 06 | 20 |  0.0240 | -0.0088\n",
      "00:49:05 07 | 01 |  0.0033 |  0.0436\n",
      "00:49:16 07 | 02 | -0.0183 | -0.0227\n",
      "00:49:26 07 | 03 |  0.0203 |  0.0377\n",
      "00:49:36 07 | 04 |  0.0268 |  0.0358\n",
      "00:49:46 07 | 05 |  0.0175 |  0.0503\n",
      "00:49:57 07 | 06 | -0.0049 |  0.0280\n",
      "00:50:07 07 | 07 |  0.0121 |  0.0418\n",
      "00:50:17 07 | 08 |  0.0209 |  0.0349\n",
      "00:50:27 07 | 09 |  0.0002 |  0.0249\n",
      "00:50:37 07 | 10 |  0.0252 |  0.0462\n",
      "00:50:48 07 | 11 |  0.0153 |  0.0314\n",
      "00:50:58 07 | 12 |  0.0204 |  0.0465\n",
      "00:51:09 07 | 13 |  0.0350 |  0.0492\n",
      "00:51:19 07 | 14 |  0.0183 |  0.0200\n",
      "00:51:30 07 | 15 |  0.0196 |  0.0261\n",
      "00:51:40 07 | 16 |  0.0218 |  0.0120\n",
      "00:51:51 07 | 17 |  0.0276 |  0.0283\n",
      "00:52:03 07 | 18 |  0.0239 |  0.0300\n",
      "00:52:15 07 | 19 |  0.0167 |  0.0221\n",
      "00:52:27 07 | 20 |  0.0241 |  0.0258\n",
      "00:52:39 08 | 01 |  0.0236 |  0.0308\n",
      "00:52:50 08 | 02 | -0.0044 |  0.0196\n",
      "00:53:01 08 | 03 | -0.0017 |  0.0022\n",
      "00:53:12 08 | 04 |  0.0148 |  0.0252\n",
      "00:53:23 08 | 05 |  0.0269 |  0.0213\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:53:35 08 | 06 |  0.0279 |  0.0255\n",
      "00:53:47 08 | 07 |  0.0276 |  0.0244\n",
      "00:53:58 08 | 08 |  0.0146 |  0.0047\n",
      "00:54:10 08 | 09 |  0.0252 |  0.0228\n",
      "00:54:21 08 | 10 |  0.0023 | -0.0058\n",
      "00:54:33 08 | 11 |  0.0105 | -0.0068\n",
      "00:54:46 08 | 12 |  0.0191 |  0.0133\n",
      "00:54:58 08 | 13 |  0.0141 |  0.0013\n",
      "00:55:11 08 | 14 | -0.0037 | -0.0133\n",
      "00:55:22 08 | 15 |  0.0212 |  0.0451\n",
      "00:55:35 08 | 16 |  0.0176 |  0.0175\n",
      "00:55:46 08 | 17 |  0.0077 |  0.0158\n",
      "00:55:57 08 | 18 |  0.0119 |  0.0132\n",
      "00:56:09 08 | 19 |  0.0027 | -0.0016\n",
      "00:56:20 08 | 20 |  0.0088 |  0.0133\n",
      "00:56:32 09 | 01 |  0.0053 |  0.0101\n",
      "00:56:43 09 | 02 |  0.0079 |  0.0253\n",
      "00:56:54 09 | 03 |  0.0021 |  0.0014\n",
      "00:57:07 09 | 04 |  0.0030 |  0.0124\n",
      "00:57:20 09 | 05 |  0.0027 |  0.0090\n",
      "00:57:34 09 | 06 |  0.0007 | -0.0173\n",
      "00:57:47 09 | 07 |  0.0021 | -0.0165\n",
      "00:58:18 09 | 08 |  0.0090 | -0.0003\n",
      "00:58:49 09 | 09 |  0.0097 |  0.0057\n",
      "00:59:19 09 | 10 |  0.0132 |  0.0174\n",
      "00:59:50 09 | 11 |  0.0110 |  0.0156\n",
      "01:00:20 09 | 12 |  0.0094 |  0.0025\n",
      "01:00:53 09 | 13 |  0.0124 |  0.0005\n",
      "01:01:26 09 | 14 |  0.0051 |  0.0081\n",
      "01:01:57 09 | 15 |  0.0115 |  0.0056\n",
      "01:02:26 09 | 16 |  0.0139 |  0.0183\n",
      "01:02:57 09 | 17 |  0.0128 | -0.0045\n",
      "01:03:27 09 | 18 |  0.0026 | -0.0032\n",
      "01:03:58 09 | 19 |  0.0057 |  0.0043\n",
      "01:04:28 09 | 20 |  0.0013 | -0.0000\n",
      "01:04:56 10 | 01 |  0.0395 |  0.0513\n",
      "01:05:07 10 | 02 | -0.0458 | -0.0716\n",
      "01:05:16 10 | 03 | -0.0004 | -0.0142\n",
      "01:05:26 10 | 04 |  0.0539 |  0.0619\n",
      "01:05:36 10 | 05 | -0.0123 | -0.0050\n",
      "01:05:46 10 | 06 | -0.0173 | -0.0164\n",
      "01:05:57 10 | 07 |  0.0150 |  0.0227\n",
      "01:06:07 10 | 08 | -0.0042 | -0.0048\n",
      "01:06:18 10 | 09 | -0.0066 |  0.0015\n",
      "01:06:30 10 | 10 | -0.0422 | -0.0544\n",
      "01:06:42 10 | 11 |  0.0326 |  0.0325\n",
      "01:06:55 10 | 12 | -0.0403 | -0.0528\n",
      "01:07:24 10 | 13 |  0.0533 |  0.0376\n",
      "01:07:53 10 | 14 |  0.0480 |  0.0325\n",
      "01:08:20 10 | 15 | -0.0396 | -0.0565\n",
      "01:08:50 10 | 16 |  0.0542 |  0.0396\n",
      "01:09:19 10 | 17 | -0.0409 | -0.0624\n",
      "01:09:44 10 | 18 | -0.0372 | -0.0551\n",
      "01:10:13 10 | 19 |  0.0585 |  0.0604\n",
      "01:10:43 10 | 20 | -0.0184 | -0.0122\n",
      "01:11:14 11 | 01 |  0.0189 |  0.0198\n",
      "01:11:52 11 | 02 |  0.0278 |  0.0204\n",
      "01:12:28 11 | 03 |  0.0086 |  0.0048\n",
      "01:13:07 11 | 04 | -0.0041 | -0.0029\n",
      "01:13:47 11 | 05 |  0.0227 |  0.0148\n",
      "01:14:26 11 | 06 |  0.0234 |  0.0131\n",
      "01:15:05 11 | 07 |  0.0265 |  0.0220\n",
      "01:15:44 11 | 08 | -0.0045 |  0.0001\n",
      "01:16:24 11 | 09 |  0.0047 |  0.0011\n",
      "01:17:03 11 | 10 |  0.0045 | -0.0008\n",
      "01:17:43 11 | 11 | -0.0170 | -0.0281\n",
      "01:18:23 11 | 12 |  0.0007 |  0.0104\n",
      "01:19:03 11 | 13 |  0.0043 | -0.0030\n",
      "01:19:42 11 | 14 | -0.0077 | -0.0200\n",
      "01:20:21 11 | 15 | -0.0082 | -0.0122\n",
      "01:21:01 11 | 16 | -0.0070 | -0.0014\n",
      "01:21:41 11 | 17 |  0.0145 |  0.0156\n",
      "01:22:21 11 | 18 | -0.0019 | -0.0083\n",
      "01:23:01 11 | 19 | -0.0054 |  0.0040\n",
      "01:23:41 11 | 20 | -0.0129 | -0.0107\n",
      "01:24:17 12 | 01 |  0.0135 |  0.0319\n",
      "01:24:55 12 | 02 |  0.0083 |  0.0018\n",
      "01:25:33 12 | 03 |  0.0060 |  0.0006\n",
      "01:26:09 12 | 04 |  0.0069 | -0.0016\n",
      "01:26:48 12 | 05 |  0.0100 |  0.0145\n",
      "01:27:33 12 | 06 |  0.0146 |  0.0148\n",
      "01:28:16 12 | 07 |  0.0095 |  0.0188\n",
      "01:28:55 12 | 08 |  0.0024 | -0.0018\n",
      "01:29:32 12 | 09 |  0.0190 |  0.0185\n",
      "01:30:10 12 | 10 |  0.0196 |  0.0214\n",
      "01:30:48 12 | 11 |  0.0143 |  0.0191\n",
      "01:31:26 12 | 12 |  0.0148 | -0.0040\n",
      "01:32:04 12 | 13 |  0.0226 |  0.0204\n",
      "01:32:43 12 | 14 |  0.0214 |  0.0300\n",
      "01:33:21 12 | 15 |  0.0265 |  0.0184\n",
      "01:33:60 12 | 16 |  0.0230 |  0.0129\n",
      "01:34:39 12 | 17 |  0.0303 |  0.0331\n",
      "01:35:18 12 | 18 |  0.0334 |  0.0262\n",
      "01:36:02 12 | 19 |  0.0169 |  0.0241\n",
      "01:36:50 12 | 20 |  0.0248 |  0.0154\n",
      "(16, 8) tanh 0 256\n",
      "00:00:14 01 | 01 |  0.0099 |  0.0171\n",
      "00:00:28 01 | 02 |  0.0164 |  0.0133\n",
      "00:00:43 01 | 03 |  0.0208 |  0.0236\n",
      "00:00:57 01 | 04 |  0.0040 |  0.0066\n",
      "00:01:10 01 | 05 |  0.0066 |  0.0029\n",
      "00:01:25 01 | 06 |  0.0036 |  0.0090\n",
      "00:01:39 01 | 07 |  0.0111 |  0.0016\n",
      "00:01:54 01 | 08 |  0.0153 |  0.0215\n",
      "00:02:07 01 | 09 |  0.0148 |  0.0100\n",
      "00:02:21 01 | 10 |  0.0066 |  0.0068\n",
      "00:02:34 01 | 11 |  0.0014 | -0.0056\n",
      "00:02:49 01 | 12 |  0.0065 | -0.0019\n",
      "00:03:03 01 | 13 |  0.0111 |  0.0083\n",
      "00:03:16 01 | 14 |  0.0158 |  0.0349\n",
      "00:03:30 01 | 15 |  0.0070 |  0.0109\n",
      "00:03:45 01 | 16 |  0.0097 |  0.0061\n",
      "00:03:59 01 | 17 |  0.0182 |  0.0071\n",
      "00:04:12 01 | 18 |  0.0065 |  0.0123\n",
      "00:04:25 01 | 19 |  0.0052 | -0.0045\n",
      "00:04:39 01 | 20 |  0.0132 |  0.0205\n",
      "00:04:55 02 | 01 |  0.0030 |  0.0036\n",
      "00:05:08 02 | 02 |  0.0051 |  0.0052\n",
      "00:05:21 02 | 03 | -0.0243 | -0.0073\n",
      "00:05:34 02 | 04 | -0.0046 | -0.0107\n",
      "00:05:49 02 | 05 | -0.0263 | -0.0138\n",
      "00:06:03 02 | 06 | -0.0230 | -0.0069\n",
      "00:06:14 02 | 07 |  0.0015 | -0.0121\n",
      "00:06:25 02 | 08 | -0.0164 | -0.0241\n",
      "00:06:37 02 | 09 | -0.0125 | -0.0103\n",
      "00:06:47 02 | 10 |  0.0227 |  0.0180\n",
      "00:06:59 02 | 11 |  0.0116 |  0.0279\n",
      "00:07:10 02 | 12 |  0.0017 |  0.0140\n",
      "00:07:21 02 | 13 | -0.0144 |  0.0117\n",
      "00:07:33 02 | 14 |  0.0134 |  0.0087\n",
      "00:07:44 02 | 15 |  0.0070 |  0.0160\n",
      "00:07:55 02 | 16 |  0.0238 |  0.0423\n",
      "00:08:06 02 | 17 | -0.0056 | -0.0059\n",
      "00:08:17 02 | 18 | -0.0100 |  0.0051\n",
      "00:08:28 02 | 19 | -0.0036 |  0.0050\n",
      "00:08:39 02 | 20 | -0.0013 | -0.0046\n",
      "00:08:52 03 | 01 |  0.0189 |  0.0225\n",
      "00:09:03 03 | 02 |  0.0162 |  0.0187\n",
      "00:09:15 03 | 03 | -0.0143 | -0.0353\n",
      "00:09:26 03 | 04 |  0.0238 |  0.0179\n",
      "00:09:37 03 | 05 |  0.0227 |  0.0328\n",
      "00:09:49 03 | 06 | -0.0182 | -0.0277\n",
      "00:10:01 03 | 07 | -0.0070 | -0.0219\n",
      "00:10:12 03 | 08 | -0.0195 | -0.0216\n",
      "00:10:23 03 | 09 |  0.0284 |  0.0307\n",
      "00:10:35 03 | 10 |  0.0265 |  0.0346\n",
      "00:10:46 03 | 11 | -0.0193 | -0.0337\n",
      "00:10:58 03 | 12 | -0.0216 | -0.0267\n",
      "00:11:09 03 | 13 | -0.0159 | -0.0086\n",
      "00:11:20 03 | 14 |  0.0057 | -0.0114\n",
      "00:11:32 03 | 15 |  0.0231 |  0.0150\n",
      "00:11:43 03 | 16 |  0.0163 |  0.0173\n",
      "00:11:54 03 | 17 |  0.0117 |  0.0300\n",
      "00:12:06 03 | 18 |  0.0183 |  0.0095\n",
      "00:12:17 03 | 19 | -0.0128 | -0.0187\n",
      "00:12:29 03 | 20 |  0.0192 |  0.0110\n",
      "00:12:41 04 | 01 | -0.0006 |  0.0141\n",
      "00:12:47 04 | 02 |  0.0140 |  0.0176\n",
      "00:12:53 04 | 03 |  0.0172 |  0.0359\n",
      "00:12:58 04 | 04 | -0.0224 | -0.0230\n",
      "00:13:03 04 | 05 |  0.0128 |  0.0197\n",
      "00:13:10 04 | 06 |  0.0038 |  0.0120\n",
      "00:13:15 04 | 07 | -0.0200 | -0.0033\n",
      "00:13:21 04 | 08 |  0.0005 | -0.0037\n",
      "00:13:25 04 | 09 | -0.0022 | -0.0037\n",
      "00:13:30 04 | 10 |  0.0458 |  0.0269\n",
      "00:13:36 04 | 11 |  0.0163 |  0.0003\n",
      "00:13:41 04 | 12 |  0.0018 | -0.0001\n",
      "00:13:47 04 | 13 | -0.0115 | -0.0177\n",
      "00:13:52 04 | 14 |  0.0143 |  0.0208\n",
      "00:13:57 04 | 15 |  0.0131 |  0.0053\n",
      "00:14:03 04 | 16 | -0.0076 | -0.0079\n",
      "00:14:08 04 | 17 | -0.0356 | -0.0557\n",
      "00:14:13 04 | 18 | -0.0088 | -0.0159\n",
      "00:14:19 04 | 19 | -0.0293 | -0.0436\n",
      "00:14:24 04 | 20 |  0.0076 |  0.0098\n",
      "00:14:31 05 | 01 |  0.0069 |  0.0045\n",
      "00:14:37 05 | 02 |  0.0001 |  0.0055\n",
      "00:14:42 05 | 03 |  0.0091 | -0.0018\n",
      "00:14:48 05 | 04 |  0.0161 | -0.0026\n",
      "00:14:54 05 | 05 | -0.0140 | -0.0132\n",
      "00:14:60 05 | 06 |  0.0148 |  0.0079\n",
      "00:15:05 05 | 07 | -0.0019 | -0.0075\n",
      "00:15:11 05 | 08 |  0.0177 |  0.0093\n",
      "00:15:17 05 | 09 |  0.0127 |  0.0135\n",
      "00:15:23 05 | 10 | -0.0016 | -0.0116\n",
      "00:15:29 05 | 11 |  0.0086 |  0.0122\n",
      "00:15:34 05 | 12 |  0.0017 |  0.0079\n",
      "00:15:40 05 | 13 |  0.0083 |  0.0040\n",
      "00:15:47 05 | 14 | -0.0052 |  0.0055\n",
      "00:15:53 05 | 15 |  0.0166 |  0.0055\n",
      "00:15:58 05 | 16 | -0.0037 |  0.0034\n",
      "00:16:04 05 | 17 |  0.0077 |  0.0060\n",
      "00:16:10 05 | 18 | -0.0007 |  0.0116\n",
      "00:16:15 05 | 19 |  0.0170 |  0.0201\n",
      "00:16:21 05 | 20 | -0.0004 |  0.0069\n",
      "00:16:28 06 | 01 |  0.0132 |  0.0082\n",
      "00:16:33 06 | 02 |  0.0079 |  0.0170\n",
      "00:16:39 06 | 03 |  0.0071 |  0.0077\n",
      "00:16:45 06 | 04 |  0.0006 |  0.0010\n",
      "00:16:51 06 | 05 |  0.0066 |  0.0072\n",
      "00:16:57 06 | 06 |  0.0217 |  0.0207\n",
      "00:17:03 06 | 07 |  0.0106 | -0.0016\n",
      "00:17:09 06 | 08 | -0.0051 | -0.0063\n",
      "00:17:15 06 | 09 |  0.0299 |  0.0094\n",
      "00:17:21 06 | 10 |  0.0274 |  0.0289\n",
      "00:17:27 06 | 11 | -0.0015 |  0.0077\n",
      "00:17:33 06 | 12 |  0.0415 |  0.0178\n",
      "00:17:39 06 | 13 |  0.0274 |  0.0205\n",
      "00:17:44 06 | 14 |  0.0345 |  0.0167\n",
      "00:17:50 06 | 15 |  0.0406 |  0.0173\n",
      "00:17:56 06 | 16 |  0.0433 |  0.0340\n",
      "00:18:02 06 | 17 |  0.0444 |  0.0328\n",
      "00:18:08 06 | 18 |  0.0016 |  0.0013\n",
      "00:18:13 06 | 19 |  0.0338 |  0.0270\n",
      "00:18:19 06 | 20 |  0.0401 |  0.0288\n",
      "00:18:26 07 | 01 | -0.0060 | -0.0030\n",
      "00:18:31 07 | 02 |  0.0214 |  0.0186\n",
      "00:18:38 07 | 03 |  0.0111 |  0.0220\n",
      "00:18:44 07 | 04 | -0.0154 |  0.0033\n",
      "00:18:50 07 | 05 |  0.0034 |  0.0371\n",
      "00:18:56 07 | 06 | -0.0120 | -0.0003\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:19:02 07 | 07 | -0.0167 |  0.0199\n",
      "00:19:08 07 | 08 | -0.0096 |  0.0117\n",
      "00:19:14 07 | 09 |  0.0278 |  0.0416\n",
      "00:19:20 07 | 10 | -0.0033 |  0.0270\n",
      "00:19:24 07 | 11 | -0.0178 |  0.0045\n",
      "00:19:30 07 | 12 |  0.0312 |  0.0420\n",
      "00:19:36 07 | 13 | -0.0062 |  0.0158\n",
      "00:19:41 07 | 14 |  0.0020 |  0.0093\n",
      "00:19:47 07 | 15 | -0.0126 | -0.0002\n",
      "00:19:53 07 | 16 | -0.0154 | -0.0019\n",
      "00:19:58 07 | 17 |  0.0203 |  0.0458\n",
      "00:20:04 07 | 18 |  0.0344 |  0.0488\n",
      "00:20:10 07 | 19 | -0.0165 | -0.0005\n",
      "00:20:15 07 | 20 | -0.0134 |  0.0031\n",
      "00:20:22 08 | 01 | -0.0048 |  0.0015\n",
      "00:20:27 08 | 02 | -0.0237 | -0.0129\n",
      "00:20:32 08 | 03 |  0.0124 |  0.0022\n",
      "00:20:38 08 | 04 |  0.0178 |  0.0239\n",
      "00:20:43 08 | 05 |  0.0275 |  0.0400\n",
      "00:20:49 08 | 06 |  0.0452 |  0.0337\n",
      "00:20:55 08 | 07 |  0.0166 |  0.0290\n",
      "00:20:60 08 | 08 |  0.0181 |  0.0267\n",
      "00:21:05 08 | 09 |  0.0366 |  0.0403\n",
      "00:21:11 08 | 10 |  0.0367 |  0.0423\n",
      "00:21:16 08 | 11 |  0.0292 |  0.0217\n",
      "00:21:22 08 | 12 |  0.0336 |  0.0259\n",
      "00:21:27 08 | 13 |  0.0222 |  0.0354\n",
      "00:21:32 08 | 14 |  0.0384 |  0.0328\n",
      "00:21:38 08 | 15 |  0.0159 |  0.0141\n",
      "00:21:44 08 | 16 |  0.0131 |  0.0298\n",
      "00:21:49 08 | 17 |  0.0095 | -0.0034\n",
      "00:21:55 08 | 18 |  0.0187 |  0.0102\n",
      "00:22:00 08 | 19 |  0.0163 |  0.0249\n",
      "00:22:07 08 | 20 |  0.0041 |  0.0100\n",
      "00:22:14 09 | 01 | -0.0118 | -0.0098\n",
      "00:22:19 09 | 02 |  0.0089 | -0.0088\n",
      "00:22:24 09 | 03 | -0.0074 | -0.0085\n",
      "00:22:30 09 | 04 |  0.0145 |  0.0060\n",
      "00:22:35 09 | 05 |  0.0066 |  0.0124\n",
      "00:22:41 09 | 06 |  0.0152 |  0.0238\n",
      "00:22:47 09 | 07 | -0.0004 | -0.0200\n",
      "00:22:51 09 | 08 |  0.0027 | -0.0159\n",
      "00:22:56 09 | 09 |  0.0051 |  0.0079\n",
      "00:23:02 09 | 10 |  0.0174 |  0.0214\n",
      "00:23:06 09 | 11 | -0.0052 | -0.0263\n",
      "00:23:12 09 | 12 |  0.0134 |  0.0093\n",
      "00:23:18 09 | 13 | -0.0096 | -0.0093\n",
      "00:23:23 09 | 14 | -0.0021 | -0.0080\n",
      "00:23:28 09 | 15 | -0.0077 | -0.0058\n",
      "00:23:34 09 | 16 | -0.0144 | -0.0060\n",
      "00:23:39 09 | 17 |  0.0014 | -0.0137\n",
      "00:23:45 09 | 18 | -0.0045 | -0.0007\n",
      "00:23:50 09 | 19 |  0.0101 |  0.0100\n",
      "00:23:55 09 | 20 | -0.0084 |  0.0029\n",
      "00:24:01 10 | 01 |  0.0124 |  0.0062\n",
      "00:24:07 10 | 02 |  0.0230 |  0.0317\n",
      "00:24:12 10 | 03 | -0.0017 |  0.0009\n",
      "00:24:18 10 | 04 | -0.0350 | -0.0382\n",
      "00:24:24 10 | 05 | -0.0180 | -0.0272\n",
      "00:24:29 10 | 06 |  0.0175 |  0.0124\n",
      "00:24:35 10 | 07 | -0.0381 | -0.0240\n",
      "00:24:40 10 | 08 | -0.0218 | -0.0310\n",
      "00:24:45 10 | 09 | -0.0257 | -0.0205\n",
      "00:24:51 10 | 10 | -0.0136 | -0.0123\n",
      "00:24:56 10 | 11 | -0.0241 | -0.0214\n",
      "00:25:08 10 | 12 | -0.0380 | -0.0241\n",
      "00:25:14 10 | 13 | -0.0187 | -0.0109\n",
      "00:25:19 10 | 14 | -0.0181 | -0.0166\n",
      "00:25:25 10 | 15 | -0.0113 | -0.0180\n",
      "00:25:31 10 | 16 |  0.0071 |  0.0021\n",
      "00:25:36 10 | 17 |  0.0098 |  0.0088\n",
      "00:25:42 10 | 18 | -0.0034 | -0.0112\n",
      "00:25:48 10 | 19 | -0.0122 | -0.0219\n",
      "00:26:01 10 | 20 | -0.0135 | -0.0028\n",
      "00:26:16 11 | 01 | -0.0018 |  0.0020\n",
      "00:26:30 11 | 02 |  0.0030 |  0.0113\n",
      "00:26:44 11 | 03 | -0.0059 | -0.0023\n",
      "00:26:58 11 | 04 |  0.0170 |  0.0262\n",
      "00:27:12 11 | 05 |  0.0124 |  0.0086\n",
      "00:27:28 11 | 06 | -0.0003 | -0.0013\n",
      "00:27:43 11 | 07 | -0.0010 | -0.0057\n",
      "00:27:56 11 | 08 | -0.0016 |  0.0095\n",
      "00:28:11 11 | 09 |  0.0079 |  0.0115\n",
      "00:28:27 11 | 10 | -0.0039 | -0.0071\n",
      "00:28:44 11 | 11 | -0.0031 | -0.0015\n",
      "00:29:01 11 | 12 |  0.0017 | -0.0046\n",
      "00:29:19 11 | 13 |  0.0135 |  0.0188\n",
      "00:29:36 11 | 14 |  0.0043 | -0.0047\n",
      "00:29:53 11 | 15 |  0.0045 |  0.0039\n",
      "00:30:10 11 | 16 |  0.0048 |  0.0004\n",
      "00:30:27 11 | 17 |  0.0043 |  0.0204\n",
      "00:30:44 11 | 18 |  0.0045 |  0.0043\n",
      "00:31:01 11 | 19 |  0.0062 |  0.0033\n",
      "00:31:18 11 | 20 |  0.0105 |  0.0088\n",
      "00:31:37 12 | 01 | -0.0120 | -0.0153\n",
      "00:31:54 12 | 02 |  0.0003 |  0.0092\n",
      "00:32:12 12 | 03 |  0.0190 |  0.0235\n",
      "00:32:29 12 | 04 |  0.0222 |  0.0350\n",
      "00:32:46 12 | 05 | -0.0030 | -0.0038\n",
      "00:33:04 12 | 06 | -0.0094 | -0.0242\n",
      "00:33:21 12 | 07 |  0.0364 |  0.0341\n",
      "00:33:38 12 | 08 |  0.0104 |  0.0215\n",
      "00:33:56 12 | 09 |  0.0179 |  0.0295\n",
      "00:34:13 12 | 10 |  0.0204 |  0.0310\n",
      "00:34:31 12 | 11 |  0.0185 |  0.0314\n",
      "00:34:48 12 | 12 |  0.0129 |  0.0036\n",
      "00:35:05 12 | 13 |  0.0159 |  0.0305\n",
      "00:35:22 12 | 14 |  0.0123 |  0.0205\n",
      "00:35:39 12 | 15 |  0.0250 |  0.0057\n",
      "00:35:56 12 | 16 |  0.0252 |  0.0328\n",
      "00:36:13 12 | 17 |  0.0127 |  0.0155\n",
      "00:36:30 12 | 18 |  0.0075 |  0.0255\n",
      "00:36:48 12 | 19 |  0.0086 |  0.0178\n",
      "00:37:04 12 | 20 |  0.0037 |  0.0193\n",
      "(32, 32) tanh 0.2 64\n",
      "00:01:01 01 | 01 | -0.0050 | -0.0027\n",
      "00:02:02 01 | 02 |  0.0067 | -0.0058\n",
      "00:03:04 01 | 03 |  0.0028 |  0.0116\n",
      "00:04:04 01 | 04 |  0.0136 |  0.0051\n",
      "00:05:04 01 | 05 | -0.0073 | -0.0161\n",
      "00:06:05 01 | 06 |  0.0223 | -0.0018\n",
      "00:06:38 01 | 07 |  0.0049 |  0.0125\n",
      "00:06:58 01 | 08 |  0.0121 |  0.0262\n",
      "00:07:18 01 | 09 | -0.0056 | -0.0008\n",
      "00:07:37 01 | 10 |  0.0138 |  0.0040\n",
      "00:07:58 01 | 11 |  0.0152 |  0.0192\n",
      "00:08:23 01 | 12 |  0.0175 |  0.0086\n",
      "00:08:43 01 | 13 |  0.0028 |  0.0122\n",
      "00:09:03 01 | 14 |  0.0177 |  0.0065\n",
      "00:09:23 01 | 15 |  0.0102 |  0.0052\n",
      "00:09:44 01 | 16 | -0.0022 |  0.0031\n",
      "00:10:05 01 | 17 |  0.0073 | -0.0028\n",
      "00:10:25 01 | 18 |  0.0078 |  0.0102\n",
      "00:10:46 01 | 19 |  0.0142 |  0.0044\n",
      "00:11:06 01 | 20 |  0.0173 |  0.0276\n",
      "00:11:29 02 | 01 | -0.0150 | -0.0027\n",
      "00:11:51 02 | 02 |  0.0046 | -0.0012\n",
      "00:12:13 02 | 03 |  0.0154 |  0.0157\n",
      "00:12:34 02 | 04 | -0.0013 | -0.0139\n",
      "00:12:54 02 | 05 |  0.0127 |  0.0068\n",
      "00:13:15 02 | 06 |  0.0225 |  0.0236\n",
      "00:13:37 02 | 07 |  0.0067 |  0.0225\n",
      "00:13:58 02 | 08 | -0.0082 | -0.0219\n",
      "00:14:19 02 | 09 |  0.0036 | -0.0228\n",
      "00:14:38 02 | 10 |  0.0181 |  0.0223\n",
      "00:14:57 02 | 11 | -0.0221 | -0.0124\n",
      "00:15:16 02 | 12 | -0.0005 |  0.0102\n",
      "00:15:36 02 | 13 | -0.0066 | -0.0138\n",
      "00:15:55 02 | 14 |  0.0153 |  0.0121\n",
      "00:16:15 02 | 15 |  0.0016 |  0.0103\n",
      "00:16:34 02 | 16 |  0.0316 |  0.0411\n",
      "00:16:53 02 | 17 | -0.0074 |  0.0072\n",
      "00:17:13 02 | 18 | -0.0061 | -0.0005\n",
      "00:17:32 02 | 19 |  0.0025 | -0.0026\n",
      "00:17:51 02 | 20 |  0.0111 |  0.0073\n",
      "00:18:10 03 | 01 | -0.0217 | -0.0177\n",
      "00:18:27 03 | 02 |  0.0016 |  0.0224\n",
      "00:18:45 03 | 03 |  0.0139 |  0.0053\n",
      "00:19:03 03 | 04 | -0.0113 | -0.0159\n",
      "00:19:21 03 | 05 |  0.0028 | -0.0210\n",
      "00:19:39 03 | 06 |  0.0053 |  0.0104\n",
      "00:19:58 03 | 07 | -0.0047 | -0.0235\n",
      "00:20:16 03 | 08 | -0.0014 |  0.0139\n",
      "00:20:34 03 | 09 |  0.0079 |  0.0213\n",
      "00:20:51 03 | 10 | -0.0131 | -0.0252\n",
      "00:21:10 03 | 11 | -0.0038 |  0.0148\n",
      "00:21:28 03 | 12 |  0.0018 | -0.0012\n",
      "00:21:46 03 | 13 |  0.0288 |  0.0268\n",
      "00:22:05 03 | 14 |  0.0180 |  0.0272\n",
      "00:22:22 03 | 15 |  0.0127 |  0.0089\n",
      "00:22:40 03 | 16 |  0.0016 | -0.0009\n",
      "00:22:59 03 | 17 | -0.0088 | -0.0182\n",
      "00:23:17 03 | 18 | -0.0164 | -0.0272\n",
      "00:23:36 03 | 19 | -0.0072 | -0.0162\n",
      "00:23:53 03 | 20 | -0.0084 | -0.0166\n",
      "00:24:12 04 | 01 |  0.0111 |  0.0064\n",
      "00:24:31 04 | 02 | -0.0147 | -0.0356\n",
      "00:24:48 04 | 03 | -0.0215 | -0.0402\n",
      "00:25:07 04 | 04 | -0.0308 | -0.0267\n",
      "00:25:26 04 | 05 | -0.0428 | -0.0533\n",
      "00:25:43 04 | 06 | -0.0251 | -0.0191\n",
      "00:26:02 04 | 07 | -0.0020 |  0.0108\n",
      "00:26:20 04 | 08 | -0.0126 | -0.0133\n",
      "00:26:39 04 | 09 | -0.0074 | -0.0157\n",
      "00:26:58 04 | 10 | -0.0077 | -0.0104\n",
      "00:27:16 04 | 11 | -0.0277 | -0.0278\n",
      "00:27:34 04 | 12 | -0.0196 | -0.0055\n",
      "00:27:53 04 | 13 | -0.0363 | -0.0357\n",
      "00:28:11 04 | 14 | -0.0244 | -0.0297\n",
      "00:28:30 04 | 15 |  0.0042 |  0.0030\n",
      "00:28:48 04 | 16 | -0.0109 | -0.0158\n",
      "00:29:06 04 | 17 | -0.0258 | -0.0491\n",
      "00:29:25 04 | 18 |  0.0210 |  0.0212\n",
      "00:29:44 04 | 19 |  0.0339 |  0.0264\n",
      "00:30:02 04 | 20 |  0.0105 | -0.0157\n",
      "00:30:21 05 | 01 |  0.0117 |  0.0115\n",
      "00:30:40 05 | 02 | -0.0235 | -0.0130\n",
      "00:30:59 05 | 03 | -0.0164 | -0.0080\n",
      "00:31:16 05 | 04 |  0.0126 | -0.0059\n",
      "00:31:35 05 | 05 |  0.0039 |  0.0066\n",
      "00:31:53 05 | 06 |  0.0207 |  0.0028\n",
      "00:32:11 05 | 07 |  0.0170 |  0.0120\n",
      "00:32:29 05 | 08 |  0.0008 |  0.0077\n",
      "00:32:48 05 | 09 | -0.0047 | -0.0005\n",
      "00:33:06 05 | 10 |  0.0095 |  0.0013\n",
      "00:33:25 05 | 11 |  0.0122 |  0.0161\n",
      "00:33:42 05 | 12 | -0.0082 | -0.0152\n",
      "00:34:01 05 | 13 |  0.0053 | -0.0013\n",
      "00:34:19 05 | 14 |  0.0062 |  0.0246\n",
      "00:34:38 05 | 15 | -0.0089 | -0.0054\n",
      "00:34:56 05 | 16 | -0.0107 | -0.0055\n",
      "00:35:14 05 | 17 | -0.0033 |  0.0042\n",
      "00:35:32 05 | 18 |  0.0022 | -0.0018\n",
      "00:35:51 05 | 19 |  0.0021 |  0.0124\n",
      "00:36:09 05 | 20 |  0.0009 | -0.0029\n",
      "00:36:29 06 | 01 |  0.0146 |  0.0190\n",
      "00:36:47 06 | 02 |  0.0297 |  0.0261\n",
      "00:37:05 06 | 03 |  0.0311 |  0.0234\n",
      "00:37:23 06 | 04 |  0.0383 |  0.0466\n",
      "00:37:40 06 | 05 |  0.0379 |  0.0339\n",
      "00:37:58 06 | 06 |  0.0170 |  0.0232\n",
      "00:38:16 06 | 07 |  0.0151 |  0.0226\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:38:35 06 | 08 |  0.0300 |  0.0342\n",
      "00:38:53 06 | 09 |  0.0298 |  0.0275\n",
      "00:39:11 06 | 10 |  0.0287 |  0.0235\n",
      "00:39:29 06 | 11 |  0.0459 |  0.0301\n",
      "00:39:47 06 | 12 |  0.0345 |  0.0263\n",
      "00:40:06 06 | 13 |  0.0477 |  0.0264\n",
      "00:40:24 06 | 14 |  0.0463 |  0.0392\n",
      "00:40:42 06 | 15 | -0.0129 |  0.0053\n",
      "00:40:60 06 | 16 |  0.0413 |  0.0491\n",
      "00:41:19 06 | 17 |  0.0340 |  0.0323\n",
      "00:41:37 06 | 18 |  0.0462 |  0.0301\n",
      "00:41:55 06 | 19 |  0.0425 |  0.0287\n",
      "00:42:13 06 | 20 |  0.0341 |  0.0249\n",
      "00:42:32 07 | 01 |  0.0030 |  0.0045\n",
      "00:42:50 07 | 02 |  0.0320 |  0.0563\n",
      "00:43:08 07 | 03 |  0.0119 |  0.0135\n",
      "00:43:27 07 | 04 |  0.0109 |  0.0575\n",
      "00:43:45 07 | 05 |  0.0303 |  0.0575\n",
      "00:44:04 07 | 06 | -0.0061 |  0.0239\n",
      "00:44:21 07 | 07 |  0.0256 |  0.0071\n",
      "00:44:39 07 | 08 |  0.0220 |  0.0241\n",
      "00:44:57 07 | 09 |  0.0302 |  0.0202\n",
      "00:45:16 07 | 10 |  0.0092 |  0.0042\n",
      "00:45:34 07 | 11 | -0.0141 | -0.0074\n",
      "00:45:53 07 | 12 |  0.0263 |  0.0307\n",
      "00:46:11 07 | 13 |  0.0053 |  0.0050\n",
      "00:46:29 07 | 14 | -0.0078 |  0.0032\n",
      "00:46:47 07 | 15 |  0.0237 |  0.0154\n",
      "00:47:06 07 | 16 |  0.0437 |  0.0753\n",
      "00:47:24 07 | 17 | -0.0015 |  0.0002\n",
      "00:47:43 07 | 18 |  0.0253 |  0.0325\n",
      "00:48:01 07 | 19 |  0.0204 |  0.0581\n",
      "00:48:19 07 | 20 | -0.0099 |  0.0479\n",
      "00:48:38 08 | 01 | -0.0142 | -0.0025\n",
      "00:48:57 08 | 02 | -0.0028 | -0.0009\n",
      "00:49:15 08 | 03 |  0.0030 | -0.0041\n",
      "00:49:34 08 | 04 | -0.0010 |  0.0175\n",
      "00:49:52 08 | 05 |  0.0406 |  0.0368\n",
      "00:50:10 08 | 06 |  0.0459 |  0.0467\n",
      "00:50:29 08 | 07 |  0.0200 |  0.0170\n",
      "00:50:46 08 | 08 |  0.0054 |  0.0012\n",
      "00:51:05 08 | 09 | -0.0026 |  0.0156\n",
      "00:51:23 08 | 10 |  0.0158 |  0.0048\n",
      "00:51:41 08 | 11 |  0.0017 |  0.0265\n",
      "00:51:59 08 | 12 |  0.0273 |  0.0294\n",
      "00:52:17 08 | 13 |  0.0099 |  0.0246\n",
      "00:52:36 08 | 14 | -0.0043 |  0.0122\n",
      "00:52:54 08 | 15 |  0.0140 |  0.0251\n",
      "00:53:12 08 | 16 |  0.0054 |  0.0049\n",
      "00:53:30 08 | 17 | -0.0130 | -0.0227\n",
      "00:53:48 08 | 18 | -0.0048 |  0.0339\n",
      "00:54:06 08 | 19 |  0.0035 |  0.0256\n",
      "00:54:25 08 | 20 |  0.0101 |  0.0159\n",
      "00:54:44 09 | 01 | -0.0022 | -0.0165\n",
      "00:55:02 09 | 02 |  0.0170 |  0.0089\n",
      "00:55:20 09 | 03 |  0.0087 |  0.0313\n",
      "00:55:38 09 | 04 |  0.0110 |  0.0241\n",
      "00:55:56 09 | 05 |  0.0157 |  0.0337\n",
      "00:56:15 09 | 06 |  0.0083 |  0.0131\n",
      "00:56:33 09 | 07 |  0.0096 |  0.0266\n",
      "00:56:52 09 | 08 |  0.0062 |  0.0005\n",
      "00:57:10 09 | 09 |  0.0106 |  0.0230\n",
      "00:57:27 09 | 10 |  0.0220 |  0.0300\n",
      "00:57:45 09 | 11 |  0.0182 |  0.0103\n",
      "00:58:03 09 | 12 |  0.0153 |  0.0205\n",
      "00:58:21 09 | 13 |  0.0017 |  0.0125\n",
      "00:58:40 09 | 14 |  0.0001 |  0.0127\n",
      "00:58:58 09 | 15 |  0.0073 |  0.0197\n",
      "00:59:16 09 | 16 |  0.0003 |  0.0081\n",
      "00:59:34 09 | 17 | -0.0026 |  0.0167\n",
      "00:59:52 09 | 18 |  0.0057 |  0.0196\n",
      "01:00:11 09 | 19 |  0.0188 |  0.0217\n",
      "01:00:29 09 | 20 |  0.0150 |  0.0176\n",
      "01:00:48 10 | 01 | -0.0118 | -0.0076\n",
      "01:01:06 10 | 02 | -0.0563 | -0.0882\n",
      "01:01:24 10 | 03 |  0.0287 |  0.0415\n",
      "01:01:42 10 | 04 |  0.0185 |  0.0343\n",
      "01:02:00 10 | 05 |  0.0139 |  0.0091\n",
      "01:02:19 10 | 06 | -0.0084 |  0.0086\n",
      "01:02:37 10 | 07 | -0.0178 | -0.0172\n",
      "01:02:55 10 | 08 | -0.0343 | -0.0231\n",
      "01:03:13 10 | 09 |  0.0060 |  0.0023\n",
      "01:03:31 10 | 10 |  0.0189 |  0.0269\n",
      "01:03:49 10 | 11 | -0.0134 | -0.0070\n",
      "01:04:09 10 | 12 |  0.0098 |  0.0071\n",
      "01:04:29 10 | 13 |  0.0291 |  0.0242\n",
      "01:04:49 10 | 14 |  0.0126 |  0.0168\n",
      "01:05:09 10 | 15 |  0.0216 |  0.0229\n",
      "01:05:29 10 | 16 | -0.0100 | -0.0203\n",
      "01:05:49 10 | 17 | -0.0169 | -0.0284\n",
      "01:06:08 10 | 18 |  0.0015 | -0.0014\n",
      "01:06:27 10 | 19 |  0.0123 |  0.0159\n",
      "01:06:47 10 | 20 |  0.0161 |  0.0109\n",
      "01:07:09 11 | 01 |  0.0282 |  0.0352\n",
      "01:07:29 11 | 02 |  0.0254 |  0.0187\n",
      "01:07:49 11 | 03 | -0.0009 | -0.0017\n",
      "01:08:08 11 | 04 |  0.0424 |  0.0355\n",
      "01:08:28 11 | 05 |  0.0381 |  0.0301\n",
      "01:08:48 11 | 06 |  0.0110 |  0.0246\n",
      "01:09:07 11 | 07 |  0.0184 |  0.0270\n",
      "01:09:27 11 | 08 |  0.0048 | -0.0026\n",
      "01:09:47 11 | 09 |  0.0277 |  0.0222\n",
      "01:10:07 11 | 10 |  0.0291 |  0.0381\n",
      "01:10:27 11 | 11 |  0.0294 |  0.0358\n",
      "01:10:47 11 | 12 |  0.0137 |  0.0161\n",
      "01:11:06 11 | 13 |  0.0321 |  0.0307\n",
      "01:11:19 11 | 14 |  0.0310 |  0.0354\n",
      "01:11:33 11 | 15 |  0.0229 |  0.0291\n",
      "01:11:46 11 | 16 |  0.0087 |  0.0113\n",
      "01:11:60 11 | 17 |  0.0352 |  0.0471\n",
      "01:12:13 11 | 18 |  0.0276 |  0.0390\n",
      "01:12:26 11 | 19 | -0.0067 | -0.0061\n",
      "01:12:40 11 | 20 |  0.0147 |  0.0175\n",
      "01:12:54 12 | 01 |  0.0191 |  0.0310\n",
      "01:13:08 12 | 02 |  0.0290 |  0.0360\n",
      "01:13:22 12 | 03 |  0.0155 |  0.0072\n",
      "01:13:38 12 | 04 |  0.0046 |  0.0077\n",
      "01:14:04 12 | 05 |  0.0104 |  0.0052\n",
      "01:14:26 12 | 06 |  0.0061 |  0.0218\n",
      "01:14:38 12 | 07 |  0.0142 |  0.0248\n",
      "01:14:49 12 | 08 |  0.0077 |  0.0021\n",
      "01:15:03 12 | 09 | -0.0000 | -0.0113\n",
      "01:15:24 12 | 10 | -0.0083 | -0.0119\n",
      "01:15:35 12 | 11 | -0.0026 | -0.0119\n",
      "01:15:48 12 | 12 | -0.0004 | -0.0007\n",
      "01:15:58 12 | 13 | -0.0014 | -0.0087\n",
      "01:16:15 12 | 14 |  0.0011 | -0.0001\n",
      "01:16:37 12 | 15 |  0.0048 |  0.0124\n",
      "01:16:59 12 | 16 | -0.0008 | -0.0179\n",
      "01:17:10 12 | 17 |  0.0013 | -0.0186\n",
      "01:17:21 12 | 18 |  0.0006 |  0.0004\n",
      "01:17:32 12 | 19 |  0.0073 |  0.0036\n",
      "01:17:43 12 | 20 |  0.0106 |  0.0069\n",
      "(32, 32) tanh 0.2 256\n",
      "00:00:04 01 | 01 |  0.0125 |  0.0141\n",
      "00:00:07 01 | 02 |  0.0158 |  0.0098\n",
      "00:00:11 01 | 03 |  0.0099 |  0.0038\n",
      "00:00:14 01 | 04 |  0.0198 |  0.0178\n",
      "00:00:17 01 | 05 |  0.0192 |  0.0183\n",
      "00:00:20 01 | 06 |  0.0158 |  0.0109\n",
      "00:00:24 01 | 07 |  0.0240 |  0.0110\n",
      "00:00:27 01 | 08 |  0.0074 |  0.0094\n",
      "00:00:30 01 | 09 |  0.0151 |  0.0212\n",
      "00:00:34 01 | 10 |  0.0066 | -0.0032\n",
      "00:00:37 01 | 11 |  0.0077 |  0.0119\n",
      "00:00:40 01 | 12 |  0.0112 |  0.0211\n",
      "00:00:44 01 | 13 |  0.0121 |  0.0066\n",
      "00:00:47 01 | 14 |  0.0069 |  0.0092\n",
      "00:00:51 01 | 15 |  0.0064 |  0.0090\n",
      "00:00:54 01 | 16 |  0.0112 |  0.0171\n",
      "00:00:58 01 | 17 |  0.0038 | -0.0061\n",
      "00:01:01 01 | 18 |  0.0014 | -0.0017\n",
      "00:01:04 01 | 19 |  0.0100 |  0.0144\n",
      "00:01:08 01 | 20 |  0.0094 |  0.0134\n",
      "00:01:12 02 | 01 |  0.0015 |  0.0162\n",
      "00:01:15 02 | 02 | -0.0175 | -0.0279\n",
      "00:01:19 02 | 03 |  0.0168 |  0.0039\n",
      "00:01:22 02 | 04 | -0.0157 | -0.0160\n",
      "00:01:25 02 | 05 |  0.0224 |  0.0433\n",
      "00:01:29 02 | 06 |  0.0012 |  0.0079\n",
      "00:01:32 02 | 07 | -0.0002 |  0.0107\n",
      "00:01:35 02 | 08 |  0.0195 |  0.0063\n",
      "00:01:38 02 | 09 |  0.0031 |  0.0087\n",
      "00:01:42 02 | 10 |  0.0077 |  0.0000\n",
      "00:01:45 02 | 11 |  0.0027 |  0.0060\n",
      "00:01:48 02 | 12 |  0.0106 |  0.0052\n",
      "00:01:52 02 | 13 |  0.0024 |  0.0127\n",
      "00:01:55 02 | 14 |  0.0094 |  0.0084\n",
      "00:01:58 02 | 15 |  0.0140 |  0.0119\n",
      "00:02:01 02 | 16 |  0.0118 |  0.0107\n",
      "00:02:04 02 | 17 |  0.0228 |  0.0055\n",
      "00:02:08 02 | 18 |  0.0088 |  0.0093\n",
      "00:02:11 02 | 19 |  0.0175 |  0.0159\n",
      "00:02:14 02 | 20 |  0.0085 |  0.0159\n",
      "00:02:18 03 | 01 | -0.0094 | -0.0167\n",
      "00:02:22 03 | 02 | -0.0039 | -0.0009\n",
      "00:02:25 03 | 03 |  0.0159 |  0.0167\n",
      "00:02:28 03 | 04 |  0.0204 |  0.0438\n",
      "00:02:32 03 | 05 |  0.0175 |  0.0212\n",
      "00:02:35 03 | 06 |  0.0001 |  0.0011\n",
      "00:02:38 03 | 07 | -0.0141 | -0.0125\n",
      "00:02:41 03 | 08 | -0.0159 | -0.0317\n",
      "00:02:45 03 | 09 | -0.0056 | -0.0008\n",
      "00:02:48 03 | 10 | -0.0062 | -0.0181\n",
      "00:02:51 03 | 11 | -0.0003 |  0.0052\n",
      "00:02:55 03 | 12 | -0.0115 | -0.0027\n",
      "00:02:58 03 | 13 |  0.0065 |  0.0001\n",
      "00:03:01 03 | 14 | -0.0066 | -0.0275\n",
      "00:03:04 03 | 15 | -0.0216 | -0.0577\n",
      "00:03:08 03 | 16 | -0.0180 | -0.0327\n",
      "00:03:11 03 | 17 |  0.0073 |  0.0040\n",
      "00:03:14 03 | 18 |  0.0008 |  0.0064\n",
      "00:03:17 03 | 19 | -0.0114 | -0.0268\n",
      "00:03:21 03 | 20 | -0.0021 |  0.0071\n",
      "00:03:25 04 | 01 |  0.0144 |  0.0144\n",
      "00:03:28 04 | 02 | -0.0316 | -0.0351\n",
      "00:03:32 04 | 03 | -0.0012 | -0.0067\n",
      "00:03:35 04 | 04 | -0.0554 | -0.0609\n",
      "00:03:38 04 | 05 |  0.0367 |  0.0365\n",
      "00:03:42 04 | 06 |  0.0021 | -0.0073\n",
      "00:03:45 04 | 07 |  0.0125 | -0.0057\n",
      "00:03:48 04 | 08 |  0.0125 |  0.0065\n",
      "00:03:51 04 | 09 |  0.0197 |  0.0020\n",
      "00:03:55 04 | 10 |  0.0182 |  0.0019\n",
      "00:03:58 04 | 11 | -0.0106 | -0.0006\n",
      "00:04:02 04 | 12 | -0.0056 | -0.0218\n",
      "00:04:06 04 | 13 | -0.0346 | -0.0439\n",
      "00:04:13 04 | 14 |  0.0054 | -0.0093\n",
      "00:04:19 04 | 15 |  0.0126 | -0.0065\n",
      "00:04:25 04 | 16 | -0.0056 | -0.0259\n",
      "00:04:31 04 | 17 | -0.0003 | -0.0183\n",
      "00:04:37 04 | 18 |  0.0000 | -0.0148\n",
      "00:04:43 04 | 19 | -0.0236 | -0.0302\n",
      "00:04:50 04 | 20 | -0.0170 | -0.0208\n",
      "00:04:55 05 | 01 |  0.0258 |  0.0188\n",
      "00:04:58 05 | 02 |  0.0284 |  0.0169\n",
      "00:05:01 05 | 03 |  0.0218 |  0.0114\n",
      "00:05:05 05 | 04 |  0.0152 |  0.0187\n",
      "00:05:08 05 | 05 |  0.0175 |  0.0239\n",
      "00:05:12 05 | 06 |  0.0036 |  0.0182\n",
      "00:05:15 05 | 07 |  0.0150 |  0.0085\n",
      "00:05:19 05 | 08 |  0.0156 |  0.0165\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:05:22 05 | 09 |  0.0142 | -0.0071\n",
      "00:05:25 05 | 10 |  0.0001 | -0.0132\n",
      "00:05:29 05 | 11 |  0.0049 |  0.0065\n",
      "00:05:32 05 | 12 |  0.0006 | -0.0019\n",
      "00:05:35 05 | 13 | -0.0115 | -0.0147\n",
      "00:05:39 05 | 14 | -0.0095 | -0.0189\n",
      "00:05:42 05 | 15 | -0.0133 | -0.0288\n",
      "00:05:45 05 | 16 | -0.0154 | -0.0165\n",
      "00:05:48 05 | 17 | -0.0118 | -0.0162\n",
      "00:05:52 05 | 18 | -0.0110 | -0.0141\n",
      "00:05:56 05 | 19 | -0.0047 | -0.0197\n",
      "00:05:59 05 | 20 | -0.0167 | -0.0449\n",
      "00:06:04 06 | 01 |  0.0151 |  0.0251\n",
      "00:06:07 06 | 02 |  0.0073 |  0.0169\n",
      "00:06:10 06 | 03 |  0.0150 |  0.0255\n",
      "00:06:14 06 | 04 |  0.0245 |  0.0232\n",
      "00:06:18 06 | 05 |  0.0234 | -0.0044\n",
      "00:06:21 06 | 06 |  0.0472 |  0.0093\n",
      "00:06:25 06 | 07 |  0.0411 |  0.0318\n",
      "00:06:28 06 | 08 |  0.0354 |  0.0286\n",
      "00:06:31 06 | 09 |  0.0465 |  0.0389\n",
      "00:06:35 06 | 10 |  0.0420 |  0.0423\n",
      "00:06:38 06 | 11 |  0.0372 |  0.0303\n",
      "00:06:41 06 | 12 |  0.0551 |  0.0415\n",
      "00:06:45 06 | 13 |  0.0301 |  0.0194\n",
      "00:06:48 06 | 14 |  0.0394 |  0.0334\n",
      "00:06:51 06 | 15 |  0.0340 |  0.0083\n",
      "00:06:55 06 | 16 |  0.0200 |  0.0082\n",
      "00:06:58 06 | 17 |  0.0252 |  0.0073\n",
      "00:07:01 06 | 18 |  0.0373 |  0.0247\n",
      "00:07:04 06 | 19 |  0.0139 | -0.0004\n",
      "00:07:08 06 | 20 |  0.0234 | -0.0002\n",
      "00:07:12 07 | 01 | -0.0051 |  0.0226\n",
      "00:07:15 07 | 02 |  0.0010 |  0.0001\n",
      "00:07:19 07 | 03 |  0.0217 |  0.0255\n",
      "00:07:22 07 | 04 |  0.0147 |  0.0048\n",
      "00:07:26 07 | 05 |  0.0272 |  0.0254\n",
      "00:07:29 07 | 06 |  0.0429 |  0.0336\n",
      "00:07:32 07 | 07 |  0.0012 |  0.0409\n",
      "00:07:36 07 | 08 | -0.0132 |  0.0158\n",
      "00:07:39 07 | 09 |  0.0020 |  0.0104\n",
      "00:07:42 07 | 10 |  0.0377 |  0.0525\n",
      "00:07:45 07 | 11 |  0.0041 |  0.0057\n",
      "00:07:49 07 | 12 |  0.0087 | -0.0162\n",
      "00:07:52 07 | 13 |  0.0170 |  0.0270\n",
      "00:07:55 07 | 14 |  0.0274 |  0.0188\n",
      "00:07:58 07 | 15 |  0.0213 |  0.0138\n",
      "00:08:01 07 | 16 |  0.0260 |  0.0106\n",
      "00:08:05 07 | 17 |  0.0211 |  0.0192\n",
      "00:08:08 07 | 18 |  0.0230 |  0.0123\n",
      "00:08:11 07 | 19 |  0.0249 |  0.0222\n",
      "00:08:15 07 | 20 |  0.0415 |  0.0377\n",
      "00:08:19 08 | 01 |  0.0137 |  0.0201\n",
      "00:08:22 08 | 02 |  0.0057 |  0.0202\n",
      "00:08:25 08 | 03 |  0.0006 | -0.0028\n",
      "00:08:28 08 | 04 |  0.0252 |  0.0278\n",
      "00:08:32 08 | 05 |  0.0186 |  0.0147\n",
      "00:08:35 08 | 06 |  0.0215 |  0.0127\n",
      "00:08:38 08 | 07 |  0.0245 |  0.0038\n",
      "00:08:41 08 | 08 |  0.0421 |  0.0261\n",
      "00:08:44 08 | 09 |  0.0255 |  0.0411\n",
      "00:08:48 08 | 10 |  0.0158 |  0.0213\n",
      "00:08:51 08 | 11 |  0.0158 |  0.0031\n",
      "00:08:54 08 | 12 |  0.0229 |  0.0194\n",
      "00:08:58 08 | 13 |  0.0164 |  0.0271\n",
      "00:09:01 08 | 14 |  0.0112 |  0.0116\n",
      "00:09:05 08 | 15 |  0.0184 |  0.0161\n",
      "00:09:08 08 | 16 |  0.0094 | -0.0092\n",
      "00:09:11 08 | 17 |  0.0068 |  0.0096\n",
      "00:09:15 08 | 18 |  0.0149 |  0.0086\n",
      "00:09:18 08 | 19 |  0.0110 | -0.0047\n",
      "00:09:21 08 | 20 |  0.0071 | -0.0178\n",
      "00:09:25 09 | 01 | -0.0060 | -0.0003\n",
      "00:09:28 09 | 02 | -0.0136 | -0.0331\n",
      "00:09:32 09 | 03 | -0.0091 | -0.0097\n",
      "00:09:35 09 | 04 |  0.0179 |  0.0174\n",
      "00:09:38 09 | 05 |  0.0270 |  0.0033\n",
      "00:09:41 09 | 06 |  0.0161 |  0.0075\n",
      "00:09:45 09 | 07 |  0.0105 |  0.0203\n",
      "00:09:49 09 | 08 |  0.0107 |  0.0091\n",
      "00:09:52 09 | 09 |  0.0017 |  0.0050\n",
      "00:09:55 09 | 10 |  0.0153 |  0.0111\n",
      "00:09:58 09 | 11 |  0.0125 |  0.0118\n",
      "00:10:02 09 | 12 |  0.0266 |  0.0145\n",
      "00:10:05 09 | 13 |  0.0264 |  0.0272\n",
      "00:10:09 09 | 14 |  0.0180 |  0.0171\n",
      "00:10:12 09 | 15 |  0.0211 |  0.0165\n",
      "00:10:15 09 | 16 |  0.0333 |  0.0351\n",
      "00:10:18 09 | 17 |  0.0342 |  0.0507\n",
      "00:10:22 09 | 18 |  0.0332 |  0.0353\n",
      "00:10:25 09 | 19 |  0.0337 |  0.0351\n",
      "00:10:28 09 | 20 |  0.0399 |  0.0398\n",
      "00:10:32 10 | 01 |  0.0175 |  0.0282\n",
      "00:10:35 10 | 02 | -0.0195 | -0.0237\n",
      "00:10:39 10 | 03 |  0.0232 |  0.0332\n",
      "00:10:42 10 | 04 |  0.0075 |  0.0101\n",
      "00:10:47 10 | 05 | -0.0191 | -0.0007\n",
      "00:10:53 10 | 06 | -0.0206 | -0.0238\n",
      "00:10:60 10 | 07 | -0.0204 | -0.0240\n",
      "00:11:06 10 | 08 | -0.0094 | -0.0029\n",
      "00:11:12 10 | 09 |  0.0092 |  0.0089\n",
      "00:11:19 10 | 10 | -0.0177 | -0.0109\n",
      "00:11:26 10 | 11 | -0.0086 |  0.0125\n",
      "00:11:32 10 | 12 |  0.0178 |  0.0107\n",
      "00:11:36 10 | 13 | -0.0127 |  0.0054\n",
      "00:11:39 10 | 14 | -0.0411 | -0.0484\n",
      "00:11:43 10 | 15 | -0.0133 | -0.0093\n",
      "00:11:46 10 | 16 | -0.0190 |  0.0067\n",
      "00:11:50 10 | 17 | -0.0367 | -0.0337\n",
      "00:11:53 10 | 18 | -0.0225 | -0.0113\n",
      "00:11:56 10 | 19 | -0.0256 | -0.0255\n",
      "00:11:60 10 | 20 | -0.0337 | -0.0260\n",
      "00:12:04 11 | 01 | -0.0003 |  0.0075\n",
      "00:12:08 11 | 02 |  0.0159 |  0.0213\n",
      "00:12:11 11 | 03 |  0.0216 |  0.0210\n",
      "00:12:15 11 | 04 |  0.0452 |  0.0521\n",
      "00:12:18 11 | 05 |  0.0198 |  0.0142\n",
      "00:12:21 11 | 06 |  0.0157 |  0.0122\n",
      "00:12:25 11 | 07 |  0.0209 |  0.0180\n",
      "00:12:31 11 | 08 | -0.0035 | -0.0127\n",
      "00:12:37 11 | 09 |  0.0367 |  0.0254\n",
      "00:12:44 11 | 10 |  0.0139 | -0.0052\n",
      "00:12:52 11 | 11 |  0.0272 |  0.0109\n",
      "00:13:01 11 | 12 |  0.0096 | -0.0004\n",
      "00:13:10 11 | 13 |  0.0058 |  0.0012\n",
      "00:13:18 11 | 14 |  0.0017 | -0.0070\n",
      "00:13:24 11 | 15 |  0.0210 |  0.0329\n",
      "00:13:29 11 | 16 |  0.0038 |  0.0024\n",
      "00:13:34 11 | 17 |  0.0109 |  0.0186\n",
      "00:13:39 11 | 18 |  0.0091 |  0.0087\n",
      "00:13:44 11 | 19 | -0.0041 | -0.0020\n",
      "00:13:49 11 | 20 |  0.0270 |  0.0147\n",
      "00:13:55 12 | 01 |  0.0096 | -0.0078\n",
      "00:14:01 12 | 02 |  0.0225 |  0.0140\n",
      "00:14:06 12 | 03 |  0.0368 |  0.0284\n",
      "00:14:11 12 | 04 |  0.0180 |  0.0147\n",
      "00:14:16 12 | 05 |  0.0217 |  0.0236\n",
      "00:14:21 12 | 06 |  0.0215 |  0.0250\n",
      "00:14:26 12 | 07 |  0.0196 |  0.0120\n",
      "00:14:31 12 | 08 |  0.0050 |  0.0036\n",
      "00:14:36 12 | 09 |  0.0055 | -0.0032\n",
      "00:14:42 12 | 10 | -0.0034 | -0.0093\n",
      "00:14:47 12 | 11 |  0.0119 | -0.0060\n",
      "00:14:52 12 | 12 |  0.0103 |  0.0224\n",
      "00:14:56 12 | 13 |  0.0007 | -0.0033\n",
      "00:15:01 12 | 14 |  0.0166 |  0.0165\n",
      "00:15:07 12 | 15 |  0.0050 |  0.0046\n",
      "00:15:12 12 | 16 |  0.0023 |  0.0059\n",
      "00:15:17 12 | 17 |  0.0146 |  0.0216\n",
      "00:15:22 12 | 18 |  0.0015 | -0.0101\n",
      "00:15:27 12 | 19 |  0.0077 |  0.0132\n",
      "00:15:32 12 | 20 |  0.0138 |  0.0032\n",
      "(32, 16) tanh 0.2 64\n",
      "00:00:18 01 | 01 |  0.0051 | -0.0096\n",
      "00:00:36 01 | 02 |  0.0085 |  0.0132\n",
      "00:00:54 01 | 03 |  0.0108 |  0.0113\n",
      "00:01:12 01 | 04 |  0.0236 |  0.0237\n",
      "00:01:29 01 | 05 |  0.0105 |  0.0202\n",
      "00:01:47 01 | 06 |  0.0018 |  0.0104\n",
      "00:02:05 01 | 07 |  0.0065 |  0.0053\n",
      "00:02:22 01 | 08 |  0.0125 |  0.0116\n",
      "00:02:39 01 | 09 |  0.0149 |  0.0140\n",
      "00:02:57 01 | 10 |  0.0126 |  0.0199\n",
      "00:03:14 01 | 11 |  0.0070 | -0.0104\n",
      "00:03:31 01 | 12 |  0.0148 |  0.0149\n",
      "00:03:48 01 | 13 |  0.0110 |  0.0137\n",
      "00:04:03 01 | 14 |  0.0218 |  0.0331\n",
      "00:04:16 01 | 15 |  0.0165 |  0.0162\n",
      "00:04:29 01 | 16 |  0.0114 |  0.0063\n",
      "00:04:43 01 | 17 |  0.0188 |  0.0141\n",
      "00:04:56 01 | 18 |  0.0153 |  0.0206\n",
      "00:05:09 01 | 19 |  0.0241 |  0.0419\n",
      "00:05:22 01 | 20 |  0.0171 |  0.0193\n",
      "00:05:36 02 | 01 | -0.0094 | -0.0270\n",
      "00:05:50 02 | 02 | -0.0138 | -0.0090\n",
      "00:06:04 02 | 03 | -0.0014 |  0.0138\n",
      "00:06:18 02 | 04 | -0.0038 | -0.0074\n",
      "00:06:34 02 | 05 |  0.0012 |  0.0083\n",
      "00:07:05 02 | 06 |  0.0127 |  0.0015\n",
      "00:07:36 02 | 07 |  0.0087 |  0.0132\n",
      "00:08:04 02 | 08 |  0.0095 |  0.0170\n",
      "00:08:30 02 | 09 | -0.0011 |  0.0008\n",
      "00:08:60 02 | 10 | -0.0037 |  0.0061\n",
      "00:09:30 02 | 11 |  0.0034 |  0.0096\n",
      "00:10:00 02 | 12 |  0.0058 |  0.0049\n",
      "00:10:31 02 | 13 | -0.0083 |  0.0002\n",
      "00:11:01 02 | 14 | -0.0053 |  0.0076\n",
      "00:11:30 02 | 15 | -0.0022 |  0.0008\n",
      "00:11:56 02 | 16 | -0.0073 |  0.0038\n",
      "00:12:54 02 | 17 | -0.0033 |  0.0085\n",
      "00:14:39 02 | 18 |  0.0087 |  0.0004\n",
      "00:16:29 02 | 19 |  0.0118 |  0.0064\n",
      "00:18:15 02 | 20 | -0.0017 | -0.0089\n",
      "00:20:07 03 | 01 | -0.0163 | -0.0277\n",
      "00:22:03 03 | 02 |  0.0288 |  0.0365\n",
      "00:23:57 03 | 03 | -0.0169 | -0.0400\n",
      "00:25:58 03 | 04 | -0.0036 |  0.0043\n",
      "00:27:27 03 | 05 | -0.0009 | -0.0030\n",
      "00:28:42 03 | 06 | -0.0015 | -0.0152\n",
      "00:29:60 03 | 07 | -0.0122 | -0.0142\n",
      "00:31:19 03 | 08 |  0.0167 |  0.0129\n",
      "00:32:33 03 | 09 |  0.0216 |  0.0223\n",
      "00:33:52 03 | 10 | -0.0001 |  0.0012\n",
      "00:35:08 03 | 11 | -0.0079 | -0.0207\n",
      "00:36:20 03 | 12 | -0.0062 | -0.0111\n",
      "00:37:28 03 | 13 | -0.0011 | -0.0003\n",
      "00:38:40 03 | 14 |  0.0170 |  0.0075\n",
      "00:39:52 03 | 15 | -0.0030 |  0.0180\n",
      "00:41:03 03 | 16 |  0.0066 |  0.0019\n",
      "00:42:08 03 | 17 |  0.0047 |  0.0043\n",
      "00:43:23 03 | 18 | -0.0046 | -0.0147\n",
      "00:44:35 03 | 19 | -0.0023 | -0.0039\n",
      "00:45:46 03 | 20 |  0.0099 |  0.0115\n",
      "00:46:27 04 | 01 | -0.0524 | -0.0686\n",
      "00:46:59 04 | 02 | -0.0152 | -0.0157\n",
      "00:47:33 04 | 03 |  0.0065 | -0.0189\n",
      "00:48:11 04 | 04 | -0.0130 | -0.0470\n",
      "00:48:45 04 | 05 | -0.0158 | -0.0338\n",
      "00:49:22 04 | 06 | -0.0187 | -0.0236\n",
      "00:49:59 04 | 07 | -0.0196 | -0.0432\n",
      "00:50:32 04 | 08 | -0.0202 | -0.0302\n",
      "00:51:15 04 | 09 | -0.0201 | -0.0453\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:51:52 04 | 10 | -0.0151 | -0.0182\n",
      "00:52:29 04 | 11 |  0.0363 |  0.0409\n",
      "00:53:05 04 | 12 | -0.0060 |  0.0090\n",
      "00:53:41 04 | 13 | -0.0422 | -0.0358\n",
      "00:54:13 04 | 14 | -0.0117 | -0.0091\n",
      "00:54:54 04 | 15 | -0.0055 | -0.0077\n",
      "00:55:31 04 | 16 | -0.0222 | -0.0253\n",
      "00:56:09 04 | 17 | -0.0195 | -0.0135\n",
      "00:56:45 04 | 18 | -0.0015 |  0.0064\n",
      "00:57:26 04 | 19 | -0.0181 | -0.0218\n",
      "00:58:03 04 | 20 | -0.0135 | -0.0200\n",
      "00:58:41 05 | 01 |  0.0213 |  0.0140\n",
      "00:59:20 05 | 02 |  0.0238 |  0.0378\n",
      "00:59:58 05 | 03 |  0.0115 |  0.0010\n",
      "01:00:36 05 | 04 |  0.0078 |  0.0053\n",
      "01:01:13 05 | 05 | -0.0118 | -0.0088\n",
      "01:01:51 05 | 06 | -0.0073 | -0.0087\n",
      "01:02:28 05 | 07 |  0.0034 |  0.0307\n",
      "01:03:06 05 | 08 | -0.0235 | -0.0261\n",
      "01:03:43 05 | 09 | -0.0180 | -0.0136\n",
      "01:04:20 05 | 10 |  0.0177 |  0.0165\n",
      "01:05:05 05 | 11 |  0.0084 |  0.0158\n",
      "01:05:32 05 | 12 | -0.0081 | -0.0235\n",
      "01:05:60 05 | 13 | -0.0027 |  0.0093\n",
      "01:06:27 05 | 14 |  0.0003 |  0.0033\n",
      "01:06:55 05 | 15 |  0.0034 |  0.0220\n",
      "01:07:23 05 | 16 |  0.0107 |  0.0149\n",
      "01:07:51 05 | 17 | -0.0099 | -0.0002\n",
      "01:08:18 05 | 18 |  0.0006 |  0.0156\n",
      "01:08:42 05 | 19 |  0.0011 |  0.0179\n",
      "01:09:10 05 | 20 |  0.0033 |  0.0017\n",
      "01:09:38 06 | 01 |  0.0327 |  0.0275\n",
      "01:10:06 06 | 02 |  0.0336 |  0.0218\n",
      "01:10:33 06 | 03 |  0.0177 |  0.0232\n",
      "01:11:00 06 | 04 |  0.0201 |  0.0355\n",
      "01:11:29 06 | 05 |  0.0238 |  0.0275\n",
      "01:11:51 06 | 06 | -0.0141 | -0.0322\n",
      "01:12:02 06 | 07 |  0.0106 |  0.0088\n",
      "01:12:13 06 | 08 |  0.0035 |  0.0172\n",
      "01:12:27 06 | 09 |  0.0200 |  0.0048\n",
      "01:12:43 06 | 10 |  0.0202 |  0.0349\n",
      "01:12:57 06 | 11 |  0.0050 |  0.0034\n",
      "01:13:07 06 | 12 |  0.0124 |  0.0205\n",
      "01:13:18 06 | 13 | -0.0008 |  0.0006\n",
      "01:13:29 06 | 14 |  0.0040 |  0.0005\n",
      "01:13:40 06 | 15 |  0.0116 |  0.0054\n",
      "01:13:50 06 | 16 | -0.0059 |  0.0010\n",
      "01:14:01 06 | 17 |  0.0043 |  0.0018\n",
      "01:14:12 06 | 18 |  0.0186 |  0.0086\n",
      "01:14:23 06 | 19 | -0.0075 |  0.0142\n",
      "01:14:34 06 | 20 | -0.0022 | -0.0015\n",
      "01:14:45 07 | 01 |  0.0104 |  0.0007\n",
      "01:14:56 07 | 02 |  0.0351 |  0.0358\n",
      "01:15:07 07 | 03 |  0.0064 |  0.0257\n",
      "01:15:17 07 | 04 | -0.0036 | -0.0032\n",
      "01:15:29 07 | 05 |  0.0221 |  0.0348\n",
      "01:15:40 07 | 06 |  0.0242 |  0.0172\n",
      "01:15:51 07 | 07 |  0.0334 |  0.0393\n",
      "01:16:02 07 | 08 |  0.0322 |  0.0369\n",
      "01:16:13 07 | 09 |  0.0246 |  0.0428\n",
      "01:16:25 07 | 10 |  0.0242 |  0.0409\n",
      "01:16:37 07 | 11 |  0.0333 |  0.0263\n",
      "01:16:48 07 | 12 |  0.0376 |  0.0296\n",
      "01:16:58 07 | 13 |  0.0223 |  0.0287\n",
      "01:17:09 07 | 14 |  0.0191 |  0.0338\n",
      "01:17:19 07 | 15 |  0.0267 |  0.0449\n",
      "01:17:30 07 | 16 |  0.0244 |  0.0493\n",
      "01:17:41 07 | 17 |  0.0163 |  0.0126\n",
      "01:17:52 07 | 18 |  0.0291 |  0.0232\n",
      "01:18:03 07 | 19 |  0.0138 |  0.0167\n",
      "01:18:13 07 | 20 |  0.0029 |  0.0122\n",
      "01:18:25 08 | 01 | -0.0094 |  0.0252\n",
      "01:18:35 08 | 02 | -0.0005 | -0.0204\n",
      "01:18:46 08 | 03 |  0.0045 |  0.0074\n",
      "01:18:57 08 | 04 |  0.0161 | -0.0020\n",
      "01:19:07 08 | 05 | -0.0076 |  0.0168\n",
      "01:19:18 08 | 06 |  0.0075 |  0.0127\n",
      "01:19:29 08 | 07 |  0.0256 |  0.0248\n",
      "01:19:40 08 | 08 |  0.0128 |  0.0377\n",
      "01:19:51 08 | 09 |  0.0186 |  0.0280\n",
      "01:20:01 08 | 10 |  0.0122 |  0.0142\n",
      "01:20:12 08 | 11 |  0.0204 |  0.0103\n",
      "01:20:24 08 | 12 |  0.0261 |  0.0442\n",
      "01:20:34 08 | 13 |  0.0147 |  0.0217\n",
      "01:20:45 08 | 14 |  0.0167 |  0.0223\n",
      "01:20:56 08 | 15 |  0.0267 |  0.0271\n",
      "01:21:06 08 | 16 |  0.0173 |  0.0162\n",
      "01:21:17 08 | 17 |  0.0141 |  0.0097\n",
      "01:21:28 08 | 18 |  0.0071 | -0.0001\n",
      "01:21:40 08 | 19 |  0.0198 |  0.0163\n",
      "01:21:50 08 | 20 |  0.0225 |  0.0474\n",
      "01:22:02 09 | 01 |  0.0260 |  0.0292\n",
      "01:22:14 09 | 02 |  0.0080 | -0.0126\n",
      "01:22:25 09 | 03 |  0.0121 |  0.0113\n",
      "01:22:41 09 | 04 |  0.0038 | -0.0018\n",
      "01:22:59 09 | 05 |  0.0029 |  0.0035\n",
      "01:23:16 09 | 06 | -0.0005 | -0.0091\n",
      "01:23:32 09 | 07 |  0.0063 |  0.0003\n",
      "01:23:48 09 | 08 |  0.0138 |  0.0122\n",
      "01:24:04 09 | 09 | -0.0144 | -0.0279\n",
      "01:24:21 09 | 10 |  0.0188 |  0.0165\n",
      "01:24:38 09 | 11 |  0.0243 |  0.0154\n",
      "01:24:54 09 | 12 |  0.0113 |  0.0298\n",
      "01:25:10 09 | 13 |  0.0088 |  0.0116\n",
      "01:25:26 09 | 14 |  0.0075 | -0.0006\n",
      "01:25:41 09 | 15 |  0.0075 |  0.0174\n",
      "01:25:57 09 | 16 |  0.0160 |  0.0140\n",
      "01:26:13 09 | 17 |  0.0028 |  0.0150\n",
      "01:26:29 09 | 18 |  0.0064 |  0.0099\n",
      "01:26:45 09 | 19 | -0.0047 |  0.0121\n",
      "01:27:01 09 | 20 |  0.0052 |  0.0082\n",
      "01:27:18 10 | 01 | -0.0247 | -0.0456\n",
      "01:27:34 10 | 02 | -0.0047 |  0.0044\n",
      "01:27:51 10 | 03 | -0.0311 | -0.0243\n",
      "01:28:08 10 | 04 |  0.0004 | -0.0276\n",
      "01:28:24 10 | 05 | -0.0113 |  0.0103\n",
      "01:28:40 10 | 06 | -0.0334 | -0.0268\n",
      "01:28:56 10 | 07 |  0.0036 | -0.0012\n",
      "01:29:13 10 | 08 | -0.0162 | -0.0172\n",
      "01:29:29 10 | 09 |  0.0079 |  0.0023\n",
      "01:29:45 10 | 10 | -0.0079 | -0.0287\n",
      "01:30:01 10 | 11 | -0.0280 | -0.0156\n",
      "01:30:17 10 | 12 | -0.0235 | -0.0294\n",
      "01:30:34 10 | 13 |  0.0082 |  0.0034\n",
      "01:30:52 10 | 14 | -0.0096 | -0.0070\n",
      "01:31:09 10 | 15 | -0.0061 | -0.0165\n",
      "01:31:26 10 | 16 | -0.0282 | -0.0434\n",
      "01:31:42 10 | 17 | -0.0177 | -0.0333\n",
      "01:31:59 10 | 18 | -0.0361 | -0.0351\n",
      "01:32:15 10 | 19 | -0.0266 | -0.0300\n",
      "01:32:32 10 | 20 | -0.0229 | -0.0242\n",
      "01:32:46 11 | 01 |  0.0230 |  0.0348\n",
      "01:32:57 11 | 02 |  0.0080 |  0.0150\n",
      "01:33:11 11 | 03 |  0.0183 |  0.0123\n",
      "01:33:26 11 | 04 |  0.0216 |  0.0084\n",
      "01:33:43 11 | 05 |  0.0389 |  0.0365\n",
      "01:33:59 11 | 06 |  0.0236 |  0.0212\n",
      "01:34:15 11 | 07 |  0.0097 |  0.0040\n",
      "01:34:32 11 | 08 |  0.0090 |  0.0042\n",
      "01:34:49 11 | 09 |  0.0175 |  0.0188\n",
      "01:35:06 11 | 10 |  0.0160 |  0.0104\n",
      "01:35:22 11 | 11 |  0.0070 | -0.0036\n",
      "01:35:39 11 | 12 | -0.0029 | -0.0166\n",
      "01:35:56 11 | 13 | -0.0136 | -0.0255\n",
      "01:36:13 11 | 14 |  0.0044 |  0.0000\n",
      "01:36:30 11 | 15 |  0.0226 |  0.0194\n",
      "01:36:47 11 | 16 |  0.0011 |  0.0012\n",
      "01:37:04 11 | 17 |  0.0223 |  0.0133\n",
      "01:37:21 11 | 18 |  0.0236 |  0.0176\n",
      "01:37:38 11 | 19 |  0.0357 |  0.0431\n",
      "01:37:55 11 | 20 |  0.0042 |  0.0040\n",
      "01:38:13 12 | 01 |  0.0084 |  0.0030\n",
      "01:38:29 12 | 02 |  0.0135 |  0.0217\n",
      "01:38:45 12 | 03 | -0.0023 | -0.0049\n",
      "01:39:03 12 | 04 |  0.0022 |  0.0103\n",
      "01:39:20 12 | 05 |  0.0031 |  0.0018\n",
      "01:39:38 12 | 06 | -0.0047 | -0.0174\n",
      "01:39:55 12 | 07 |  0.0068 |  0.0019\n",
      "01:40:12 12 | 08 |  0.0128 |  0.0003\n",
      "01:40:29 12 | 09 |  0.0212 |  0.0162\n",
      "01:40:46 12 | 10 |  0.0199 |  0.0254\n",
      "01:41:03 12 | 11 |  0.0147 |  0.0239\n",
      "01:41:20 12 | 12 |  0.0150 |  0.0070\n",
      "01:41:37 12 | 13 | -0.0017 |  0.0158\n",
      "01:41:54 12 | 14 |  0.0059 |  0.0170\n",
      "01:42:11 12 | 15 | -0.0052 | -0.0160\n",
      "01:42:28 12 | 16 | -0.0025 |  0.0020\n",
      "01:42:45 12 | 17 |  0.0076 | -0.0017\n",
      "01:43:02 12 | 18 |  0.0040 |  0.0035\n",
      "01:43:19 12 | 19 | -0.0036 |  0.0131\n",
      "01:43:35 12 | 20 | -0.0071 | -0.0029\n",
      "(32, 16) tanh 0.2 256\n",
      "00:00:06 01 | 01 |  0.0105 |  0.0049\n",
      "00:00:11 01 | 02 |  0.0195 |  0.0299\n",
      "00:00:16 01 | 03 |  0.0198 |  0.0226\n",
      "00:00:21 01 | 04 |  0.0067 |  0.0129\n",
      "00:00:25 01 | 05 |  0.0174 |  0.0225\n",
      "00:00:30 01 | 06 | -0.0019 |  0.0089\n",
      "00:00:35 01 | 07 | -0.0056 | -0.0142\n",
      "00:00:40 01 | 08 | -0.0003 |  0.0152\n",
      "00:00:46 01 | 09 | -0.0042 | -0.0032\n",
      "00:00:51 01 | 10 |  0.0036 |  0.0141\n",
      "00:00:57 01 | 11 | -0.0000 |  0.0147\n",
      "00:01:02 01 | 12 | -0.0012 |  0.0070\n",
      "00:01:07 01 | 13 |  0.0072 |  0.0175\n",
      "00:01:12 01 | 14 |  0.0106 |  0.0107\n",
      "00:01:17 01 | 15 |  0.0080 |  0.0168\n",
      "00:01:23 01 | 16 |  0.0004 |  0.0029\n",
      "00:01:28 01 | 17 |  0.0182 |  0.0159\n",
      "00:01:33 01 | 18 |  0.0146 |  0.0081\n",
      "00:01:39 01 | 19 |  0.0228 |  0.0123\n",
      "00:01:45 01 | 20 |  0.0159 |  0.0189\n",
      "00:01:51 02 | 01 | -0.0183 | -0.0063\n",
      "00:01:56 02 | 02 |  0.0007 |  0.0011\n",
      "00:02:01 02 | 03 | -0.0063 |  0.0065\n",
      "00:02:06 02 | 04 | -0.0201 | -0.0088\n",
      "00:02:12 02 | 05 | -0.0062 |  0.0003\n",
      "00:02:17 02 | 06 | -0.0062 |  0.0009\n",
      "00:02:22 02 | 07 |  0.0028 |  0.0050\n",
      "00:02:27 02 | 08 |  0.0074 |  0.0215\n",
      "00:02:32 02 | 09 | -0.0074 | -0.0043\n",
      "00:02:37 02 | 10 | -0.0060 | -0.0091\n",
      "00:02:42 02 | 11 |  0.0061 |  0.0086\n",
      "00:02:48 02 | 12 | -0.0100 | -0.0146\n",
      "00:02:53 02 | 13 |  0.0019 |  0.0007\n",
      "00:02:58 02 | 14 |  0.0087 | -0.0033\n",
      "00:03:03 02 | 15 |  0.0088 |  0.0077\n",
      "00:03:08 02 | 16 | -0.0013 | -0.0013\n",
      "00:03:13 02 | 17 |  0.0087 |  0.0196\n",
      "00:03:18 02 | 18 | -0.0022 |  0.0027\n",
      "00:03:23 02 | 19 | -0.0047 | -0.0051\n",
      "00:03:28 02 | 20 |  0.0094 |  0.0237\n",
      "00:03:34 03 | 01 | -0.0057 | -0.0092\n",
      "00:03:39 03 | 02 |  0.0229 |  0.0094\n",
      "00:03:44 03 | 03 |  0.0108 | -0.0050\n",
      "00:03:49 03 | 04 |  0.0001 |  0.0087\n",
      "00:03:54 03 | 05 |  0.0113 |  0.0193\n",
      "00:03:59 03 | 06 | -0.0170 | -0.0250\n",
      "00:04:04 03 | 07 |  0.0026 | -0.0143\n",
      "00:04:09 03 | 08 |  0.0111 |  0.0154\n",
      "00:04:15 03 | 09 |  0.0128 |  0.0191\n",
      "00:04:20 03 | 10 |  0.0046 | -0.0066\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:04:25 03 | 11 | -0.0192 | -0.0188\n",
      "00:04:30 03 | 12 | -0.0103 | -0.0143\n",
      "00:04:35 03 | 13 | -0.0103 | -0.0141\n",
      "00:04:40 03 | 14 | -0.0074 |  0.0032\n",
      "00:04:45 03 | 15 | -0.0156 | -0.0219\n",
      "00:04:50 03 | 16 | -0.0192 | -0.0366\n",
      "00:04:56 03 | 17 | -0.0096 | -0.0179\n",
      "00:05:01 03 | 18 | -0.0085 | -0.0150\n",
      "00:05:06 03 | 19 | -0.0103 | -0.0163\n",
      "00:05:11 03 | 20 | -0.0155 | -0.0270\n",
      "00:05:17 04 | 01 |  0.0010 | -0.0013\n",
      "00:05:22 04 | 02 | -0.0297 | -0.0455\n",
      "00:05:27 04 | 03 | -0.0180 | -0.0164\n",
      "00:05:32 04 | 04 |  0.0011 | -0.0005\n",
      "00:05:37 04 | 05 |  0.0348 |  0.0232\n",
      "00:05:42 04 | 06 | -0.0002 |  0.0019\n",
      "00:05:47 04 | 07 | -0.0034 |  0.0056\n",
      "00:05:52 04 | 08 |  0.0056 |  0.0027\n",
      "00:05:57 04 | 09 | -0.0151 | -0.0213\n",
      "00:06:02 04 | 10 | -0.0201 | -0.0397\n",
      "00:06:08 04 | 11 | -0.0105 | -0.0261\n",
      "00:06:13 04 | 12 |  0.0227 |  0.0199\n",
      "00:06:18 04 | 13 |  0.0132 | -0.0028\n",
      "00:06:23 04 | 14 |  0.0086 | -0.0038\n",
      "00:06:28 04 | 15 |  0.0343 |  0.0414\n",
      "00:06:33 04 | 16 |  0.0396 |  0.0317\n",
      "00:06:38 04 | 17 |  0.0181 | -0.0057\n",
      "00:06:43 04 | 18 |  0.0285 |  0.0218\n",
      "00:06:48 04 | 19 |  0.0217 |  0.0038\n",
      "00:06:53 04 | 20 |  0.0148 | -0.0018\n",
      "00:06:60 05 | 01 |  0.0267 |  0.0057\n",
      "00:07:05 05 | 02 |  0.0110 |  0.0079\n",
      "00:07:10 05 | 03 |  0.0035 | -0.0111\n",
      "00:07:15 05 | 04 |  0.0116 |  0.0088\n",
      "00:07:20 05 | 05 |  0.0012 | -0.0041\n",
      "00:07:25 05 | 06 |  0.0293 |  0.0088\n",
      "00:07:30 05 | 07 |  0.0150 | -0.0019\n",
      "00:07:35 05 | 08 | -0.0010 |  0.0196\n",
      "00:07:40 05 | 09 |  0.0034 |  0.0059\n",
      "00:07:46 05 | 10 |  0.0181 |  0.0215\n",
      "00:07:51 05 | 11 |  0.0040 | -0.0150\n",
      "00:07:56 05 | 12 | -0.0006 | -0.0144\n",
      "00:08:01 05 | 13 |  0.0030 | -0.0142\n",
      "00:08:06 05 | 14 | -0.0026 | -0.0104\n",
      "00:08:12 05 | 15 |  0.0141 | -0.0001\n",
      "00:08:17 05 | 16 |  0.0070 |  0.0014\n",
      "00:08:23 05 | 17 |  0.0040 |  0.0148\n",
      "00:08:29 05 | 18 | -0.0021 | -0.0122\n",
      "00:08:37 05 | 19 |  0.0002 | -0.0050\n",
      "00:08:45 05 | 20 | -0.0124 | -0.0129\n",
      "00:08:54 06 | 01 |  0.0128 |  0.0058\n",
      "00:09:01 06 | 02 |  0.0204 |  0.0398\n",
      "00:09:09 06 | 03 |  0.0308 |  0.0279\n",
      "00:09:14 06 | 04 |  0.0278 |  0.0114\n",
      "00:09:19 06 | 05 |  0.0369 |  0.0234\n",
      "00:09:25 06 | 06 |  0.0422 |  0.0268\n",
      "00:09:31 06 | 07 |  0.0373 |  0.0174\n",
      "00:09:36 06 | 08 |  0.0173 |  0.0194\n",
      "00:09:41 06 | 09 |  0.0353 |  0.0335\n",
      "00:09:47 06 | 10 |  0.0253 |  0.0276\n",
      "00:09:53 06 | 11 |  0.0335 |  0.0146\n",
      "00:10:01 06 | 12 |  0.0048 | -0.0046\n",
      "00:10:09 06 | 13 |  0.0199 |  0.0035\n",
      "00:10:17 06 | 14 |  0.0063 |  0.0112\n",
      "00:10:24 06 | 15 |  0.0164 |  0.0172\n",
      "00:10:32 06 | 16 |  0.0250 |  0.0163\n",
      "00:10:39 06 | 17 |  0.0122 |  0.0202\n",
      "00:10:46 06 | 18 |  0.0279 |  0.0173\n",
      "00:10:53 06 | 19 |  0.0305 |  0.0174\n",
      "00:11:01 06 | 20 | -0.0028 | -0.0003\n",
      "00:11:09 07 | 01 |  0.0208 |  0.0196\n",
      "00:11:17 07 | 02 |  0.0256 |  0.0469\n",
      "00:11:24 07 | 03 |  0.0315 |  0.0409\n",
      "00:11:31 07 | 04 |  0.0137 |  0.0391\n",
      "00:11:39 07 | 05 |  0.0171 |  0.0317\n",
      "00:11:46 07 | 06 |  0.0239 |  0.0376\n",
      "00:11:54 07 | 07 |  0.0217 |  0.0602\n",
      "00:12:02 07 | 08 |  0.0325 |  0.0506\n",
      "00:12:10 07 | 09 |  0.0187 |  0.0224\n",
      "00:12:18 07 | 10 |  0.0168 |  0.0368\n",
      "00:12:26 07 | 11 |  0.0320 |  0.0328\n",
      "00:12:33 07 | 12 |  0.0216 |  0.0288\n",
      "00:12:40 07 | 13 |  0.0294 |  0.0274\n",
      "00:12:48 07 | 14 |  0.0135 |  0.0190\n",
      "00:12:55 07 | 15 |  0.0268 |  0.0310\n",
      "00:13:00 07 | 16 |  0.0223 |  0.0346\n",
      "00:13:05 07 | 17 |  0.0245 |  0.0346\n",
      "00:13:10 07 | 18 |  0.0077 |  0.0177\n",
      "00:13:15 07 | 19 |  0.0144 |  0.0174\n",
      "00:13:20 07 | 20 |  0.0225 |  0.0233\n",
      "00:13:26 08 | 01 | -0.0086 | -0.0045\n",
      "00:13:31 08 | 02 | -0.0098 |  0.0013\n",
      "00:13:36 08 | 03 | -0.0008 |  0.0004\n",
      "00:13:41 08 | 04 |  0.0016 | -0.0218\n",
      "00:13:46 08 | 05 |  0.0191 |  0.0134\n",
      "00:13:51 08 | 06 |  0.0332 |  0.0351\n",
      "00:13:56 08 | 07 |  0.0168 | -0.0058\n",
      "00:14:01 08 | 08 |  0.0263 |  0.0368\n",
      "00:14:05 08 | 09 |  0.0325 |  0.0202\n",
      "00:14:10 08 | 10 |  0.0432 |  0.0224\n",
      "00:14:15 08 | 11 |  0.0461 |  0.0340\n",
      "00:14:20 08 | 12 |  0.0333 |  0.0161\n",
      "00:14:25 08 | 13 |  0.0429 |  0.0177\n",
      "00:14:30 08 | 14 |  0.0515 |  0.0173\n",
      "00:14:35 08 | 15 |  0.0467 |  0.0331\n",
      "00:14:40 08 | 16 |  0.0340 |  0.0171\n",
      "00:14:45 08 | 17 |  0.0413 |  0.0263\n",
      "00:14:50 08 | 18 |  0.0346 |  0.0124\n",
      "00:14:55 08 | 19 |  0.0302 |  0.0115\n",
      "00:15:00 08 | 20 |  0.0416 |  0.0200\n",
      "00:15:06 09 | 01 |  0.0079 |  0.0222\n",
      "00:15:11 09 | 02 |  0.0256 |  0.0349\n",
      "00:15:15 09 | 03 | -0.0007 | -0.0068\n",
      "00:15:20 09 | 04 |  0.0110 | -0.0127\n",
      "00:15:25 09 | 05 |  0.0011 | -0.0086\n",
      "00:15:30 09 | 06 | -0.0034 | -0.0030\n",
      "00:15:35 09 | 07 |  0.0010 |  0.0052\n",
      "00:15:40 09 | 08 |  0.0169 |  0.0127\n",
      "00:15:46 09 | 09 |  0.0035 |  0.0150\n",
      "00:15:51 09 | 10 |  0.0068 |  0.0097\n",
      "00:15:56 09 | 11 |  0.0147 |  0.0326\n",
      "00:16:01 09 | 12 |  0.0059 |  0.0019\n",
      "00:16:06 09 | 13 |  0.0137 |  0.0306\n",
      "00:16:11 09 | 14 |  0.0094 |  0.0300\n",
      "00:16:17 09 | 15 |  0.0058 |  0.0151\n",
      "00:16:22 09 | 16 |  0.0156 |  0.0403\n",
      "00:16:27 09 | 17 |  0.0040 |  0.0059\n",
      "00:16:32 09 | 18 | -0.0013 | -0.0294\n",
      "00:16:37 09 | 19 | -0.0014 | -0.0048\n",
      "00:16:42 09 | 20 |  0.0022 |  0.0036\n",
      "00:16:48 10 | 01 | -0.0189 | -0.0289\n",
      "00:16:53 10 | 02 | -0.0036 | -0.0122\n",
      "00:16:58 10 | 03 |  0.0077 |  0.0012\n",
      "00:17:03 10 | 04 |  0.0202 |  0.0121\n",
      "00:17:08 10 | 05 | -0.0298 | -0.0491\n",
      "00:17:13 10 | 06 | -0.0366 | -0.0183\n",
      "00:17:18 10 | 07 | -0.0408 | -0.0482\n",
      "00:17:23 10 | 08 | -0.0108 | -0.0044\n",
      "00:17:28 10 | 09 | -0.0311 | -0.0586\n",
      "00:17:33 10 | 10 | -0.0285 | -0.0135\n",
      "00:17:38 10 | 11 | -0.0449 | -0.0553\n",
      "00:17:43 10 | 12 | -0.0275 | -0.0151\n",
      "00:17:49 10 | 13 | -0.0180 | -0.0185\n",
      "00:17:54 10 | 14 | -0.0115 | -0.0209\n",
      "00:17:59 10 | 15 | -0.0340 | -0.0291\n",
      "00:18:04 10 | 16 | -0.0207 | -0.0177\n",
      "00:18:09 10 | 17 | -0.0475 | -0.0364\n",
      "00:18:14 10 | 18 | -0.0289 | -0.0137\n",
      "00:18:19 10 | 19 | -0.0433 | -0.0329\n",
      "00:18:25 10 | 20 | -0.0153 | -0.0059\n",
      "00:18:31 11 | 01 |  0.0307 |  0.0310\n",
      "00:18:36 11 | 02 |  0.0264 |  0.0279\n",
      "00:18:41 11 | 03 |  0.0350 |  0.0286\n",
      "00:18:46 11 | 04 |  0.0301 |  0.0282\n",
      "00:18:51 11 | 05 |  0.0335 |  0.0348\n",
      "00:18:56 11 | 06 | -0.0062 | -0.0235\n",
      "00:19:01 11 | 07 |  0.0156 |  0.0132\n",
      "00:19:06 11 | 08 | -0.0066 |  0.0061\n",
      "00:19:11 11 | 09 |  0.0036 |  0.0014\n",
      "00:19:16 11 | 10 |  0.0240 |  0.0228\n",
      "00:19:21 11 | 11 |  0.0322 |  0.0290\n",
      "00:19:26 11 | 12 | -0.0178 | -0.0167\n",
      "00:19:31 11 | 13 | -0.0040 | -0.0095\n",
      "00:19:36 11 | 14 | -0.0060 | -0.0095\n",
      "00:19:41 11 | 15 |  0.0020 |  0.0099\n",
      "00:19:46 11 | 16 |  0.0102 |  0.0149\n",
      "00:19:51 11 | 17 |  0.0144 |  0.0228\n",
      "00:19:56 11 | 18 |  0.0012 |  0.0088\n",
      "00:20:01 11 | 19 | -0.0038 | -0.0033\n",
      "00:20:07 11 | 20 |  0.0093 |  0.0154\n",
      "00:20:12 12 | 01 |  0.0044 |  0.0161\n",
      "00:20:17 12 | 02 |  0.0120 |  0.0132\n",
      "00:20:22 12 | 03 |  0.0238 |  0.0188\n",
      "00:20:27 12 | 04 |  0.0120 |  0.0228\n",
      "00:20:32 12 | 05 |  0.0027 |  0.0204\n",
      "00:20:37 12 | 06 |  0.0064 |  0.0060\n",
      "00:20:42 12 | 07 |  0.0180 |  0.0064\n",
      "00:20:47 12 | 08 |  0.0200 |  0.0057\n",
      "00:20:52 12 | 09 |  0.0133 |  0.0284\n",
      "00:20:56 12 | 10 |  0.0176 |  0.0280\n",
      "00:21:02 12 | 11 |  0.0069 | -0.0174\n",
      "00:21:07 12 | 12 |  0.0075 |  0.0004\n",
      "00:21:13 12 | 13 |  0.0140 | -0.0257\n",
      "00:21:20 12 | 14 |  0.0082 | -0.0230\n",
      "00:21:26 12 | 15 |  0.0141 |  0.0010\n",
      "00:21:33 12 | 16 |  0.0021 | -0.0241\n",
      "00:21:40 12 | 17 |  0.0014 | -0.0218\n",
      "00:21:46 12 | 18 |  0.0082 |  0.0150\n",
      "00:21:53 12 | 19 |  0.0012 | -0.0142\n",
      "00:21:59 12 | 20 |  0.0028 | -0.0227\n",
      "(16, 8) tanh 0.2 64\n",
      "00:00:23 01 | 01 |  0.0114 |  0.0104\n",
      "00:00:45 01 | 02 |  0.0181 |  0.0131\n",
      "00:01:07 01 | 03 |  0.0107 |  0.0090\n",
      "00:01:31 01 | 04 |  0.0049 |  0.0121\n",
      "00:01:53 01 | 05 |  0.0179 |  0.0236\n",
      "00:02:09 01 | 06 |  0.0230 |  0.0245\n",
      "00:02:24 01 | 07 |  0.0155 |  0.0245\n",
      "00:02:40 01 | 08 |  0.0044 |  0.0150\n",
      "00:02:55 01 | 09 |  0.0172 |  0.0186\n",
      "00:03:11 01 | 10 |  0.0188 |  0.0172\n",
      "00:03:26 01 | 11 |  0.0153 |  0.0241\n",
      "00:03:42 01 | 12 |  0.0059 |  0.0044\n",
      "00:03:57 01 | 13 |  0.0075 | -0.0066\n",
      "00:04:14 01 | 14 |  0.0039 | -0.0047\n",
      "00:04:29 01 | 15 |  0.0114 |  0.0330\n",
      "00:04:45 01 | 16 |  0.0022 |  0.0131\n",
      "00:05:00 01 | 17 | -0.0002 | -0.0151\n",
      "00:05:16 01 | 18 | -0.0024 | -0.0121\n",
      "00:05:31 01 | 19 | -0.0024 | -0.0126\n",
      "00:05:47 01 | 20 | -0.0054 | -0.0041\n",
      "00:06:04 02 | 01 |  0.0283 |  0.0267\n",
      "00:06:20 02 | 02 |  0.0245 |  0.0229\n",
      "00:06:36 02 | 03 | -0.0069 |  0.0001\n",
      "00:06:52 02 | 04 |  0.0097 |  0.0160\n",
      "00:07:08 02 | 05 |  0.0198 |  0.0173\n",
      "00:07:24 02 | 06 |  0.0194 |  0.0235\n",
      "00:07:40 02 | 07 |  0.0058 | -0.0047\n",
      "00:07:57 02 | 08 |  0.0150 |  0.0190\n",
      "00:08:19 02 | 09 | -0.0024 | -0.0016\n",
      "00:08:40 02 | 10 | -0.0054 | -0.0063\n",
      "00:09:01 02 | 11 |  0.0009 |  0.0225\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:09:23 02 | 12 |  0.0037 |  0.0091\n",
      "00:09:46 02 | 13 |  0.0099 |  0.0203\n",
      "00:10:07 02 | 14 | -0.0007 |  0.0057\n",
      "00:10:41 02 | 15 |  0.0062 |  0.0125\n",
      "00:11:16 02 | 16 | -0.0090 | -0.0013\n",
      "00:11:50 02 | 17 | -0.0016 |  0.0065\n",
      "00:12:23 02 | 18 | -0.0075 | -0.0048\n",
      "00:12:57 02 | 19 |  0.0060 |  0.0139\n",
      "00:13:29 02 | 20 |  0.0052 |  0.0123\n",
      "00:14:04 03 | 01 | -0.0009 | -0.0149\n",
      "00:14:42 03 | 02 | -0.0070 | -0.0007\n",
      "00:15:19 03 | 03 |  0.0004 |  0.0071\n",
      "00:15:56 03 | 04 |  0.0136 |  0.0183\n",
      "00:16:30 03 | 05 | -0.0010 | -0.0069\n",
      "00:17:04 03 | 06 | -0.0087 | -0.0158\n",
      "00:17:38 03 | 07 | -0.0092 | -0.0068\n",
      "00:18:13 03 | 08 | -0.0025 |  0.0060\n",
      "00:18:46 03 | 09 |  0.0031 |  0.0094\n",
      "00:19:20 03 | 10 |  0.0209 | -0.0036\n",
      "00:19:51 03 | 11 |  0.0106 |  0.0208\n",
      "00:20:14 03 | 12 |  0.0161 |  0.0062\n",
      "00:20:55 03 | 13 |  0.0066 |  0.0104\n",
      "00:21:33 03 | 14 |  0.0111 |  0.0064\n",
      "00:22:17 03 | 15 |  0.0108 | -0.0018\n",
      "00:23:00 03 | 16 |  0.0069 | -0.0149\n",
      "00:23:43 03 | 17 |  0.0129 |  0.0144\n",
      "00:24:00 03 | 18 |  0.0038 | -0.0005\n",
      "00:24:16 03 | 19 |  0.0007 | -0.0030\n",
      "00:24:32 03 | 20 |  0.0040 |  0.0115\n",
      "00:24:49 04 | 01 | -0.0378 | -0.0454\n",
      "00:25:04 04 | 02 | -0.0175 | -0.0203\n",
      "00:25:21 04 | 03 |  0.0292 |  0.0212\n",
      "00:25:38 04 | 04 |  0.0210 | -0.0085\n",
      "00:25:54 04 | 05 |  0.0225 |  0.0103\n",
      "00:26:10 04 | 06 |  0.0176 |  0.0102\n",
      "00:26:27 04 | 07 | -0.0025 | -0.0192\n",
      "00:26:43 04 | 08 | -0.0132 | -0.0095\n",
      "00:26:59 04 | 09 |  0.0132 | -0.0064\n",
      "00:27:15 04 | 10 |  0.0064 | -0.0091\n",
      "00:27:30 04 | 11 | -0.0081 | -0.0033\n",
      "00:27:46 04 | 12 | -0.0007 |  0.0049\n",
      "00:28:02 04 | 13 | -0.0053 | -0.0104\n",
      "00:28:18 04 | 14 | -0.0010 | -0.0011\n",
      "00:28:34 04 | 15 | -0.0117 | -0.0295\n",
      "00:28:51 04 | 16 | -0.0232 | -0.0425\n",
      "00:29:07 04 | 17 | -0.0056 | -0.0054\n",
      "00:29:23 04 | 18 | -0.0064 | -0.0068\n",
      "00:29:39 04 | 19 | -0.0189 | -0.0192\n",
      "00:29:55 04 | 20 | -0.0160 | -0.0012\n",
      "00:30:12 05 | 01 |  0.0100 |  0.0098\n",
      "00:30:28 05 | 02 |  0.0213 |  0.0177\n",
      "00:30:44 05 | 03 |  0.0048 | -0.0092\n",
      "00:31:00 05 | 04 | -0.0174 | -0.0245\n",
      "00:31:16 05 | 05 | -0.0017 |  0.0090\n",
      "00:31:33 05 | 06 | -0.0140 | -0.0024\n",
      "00:31:48 05 | 07 | -0.0034 |  0.0066\n",
      "00:32:05 05 | 08 | -0.0207 | -0.0328\n",
      "00:32:21 05 | 09 |  0.0004 |  0.0273\n",
      "00:32:37 05 | 10 | -0.0157 | -0.0111\n",
      "00:32:51 05 | 11 | -0.0238 | -0.0192\n",
      "00:33:05 05 | 12 | -0.0049 |  0.0083\n",
      "00:33:22 05 | 13 |  0.0096 |  0.0118\n",
      "00:33:39 05 | 14 |  0.0058 |  0.0228\n",
      "00:33:56 05 | 15 | -0.0017 |  0.0171\n",
      "00:34:09 05 | 16 | -0.0161 | -0.0054\n",
      "00:34:21 05 | 17 |  0.0061 |  0.0174\n",
      "00:34:32 05 | 18 | -0.0209 | -0.0113\n",
      "00:34:43 05 | 19 | -0.0062 | -0.0090\n",
      "00:34:54 05 | 20 | -0.0179 | -0.0081\n",
      "00:35:06 06 | 01 |  0.0180 |  0.0416\n",
      "00:35:17 06 | 02 |  0.0185 | -0.0102\n",
      "00:35:28 06 | 03 |  0.0273 |  0.0051\n",
      "00:35:39 06 | 04 |  0.0332 |  0.0340\n",
      "00:35:50 06 | 05 |  0.0522 |  0.0321\n",
      "00:36:00 06 | 06 |  0.0505 |  0.0390\n",
      "00:36:12 06 | 07 |  0.0406 |  0.0030\n",
      "00:36:24 06 | 08 |  0.0587 |  0.0362\n",
      "00:36:35 06 | 09 |  0.0379 |  0.0298\n",
      "00:36:46 06 | 10 |  0.0489 |  0.0309\n",
      "00:36:56 06 | 11 |  0.0536 |  0.0271\n",
      "00:37:08 06 | 12 |  0.0415 |  0.0279\n",
      "00:37:19 06 | 13 |  0.0446 |  0.0325\n",
      "00:37:30 06 | 14 |  0.0541 |  0.0262\n",
      "00:37:40 06 | 15 |  0.0272 |  0.0127\n",
      "00:37:52 06 | 16 |  0.0419 |  0.0158\n",
      "00:38:04 06 | 17 |  0.0469 |  0.0363\n",
      "00:38:16 06 | 18 |  0.0413 |  0.0281\n",
      "00:38:27 06 | 19 |  0.0415 |  0.0383\n",
      "00:38:37 06 | 20 |  0.0421 |  0.0441\n",
      "00:38:49 07 | 01 |  0.0050 |  0.0149\n",
      "00:38:60 07 | 02 |  0.0297 |  0.0414\n",
      "00:39:11 07 | 03 |  0.0213 |  0.0324\n",
      "00:39:22 07 | 04 |  0.0360 |  0.0634\n",
      "00:39:33 07 | 05 |  0.0239 |  0.0355\n",
      "00:39:44 07 | 06 |  0.0225 |  0.0294\n",
      "00:39:55 07 | 07 |  0.0521 |  0.0471\n",
      "00:40:06 07 | 08 |  0.0290 |  0.0380\n",
      "00:40:17 07 | 09 |  0.0140 |  0.0251\n",
      "00:40:28 07 | 10 |  0.0109 |  0.0194\n",
      "00:40:39 07 | 11 |  0.0129 |  0.0037\n",
      "00:40:50 07 | 12 |  0.0356 |  0.0425\n",
      "00:41:01 07 | 13 | -0.0008 |  0.0044\n",
      "00:41:12 07 | 14 | -0.0083 |  0.0282\n",
      "00:41:23 07 | 15 |  0.0026 |  0.0215\n",
      "00:41:34 07 | 16 | -0.0014 |  0.0303\n",
      "00:41:45 07 | 17 |  0.0201 |  0.0274\n",
      "00:41:56 07 | 18 | -0.0119 | -0.0083\n",
      "00:42:07 07 | 19 |  0.0050 |  0.0383\n",
      "00:42:18 07 | 20 |  0.0151 |  0.0105\n",
      "00:42:30 08 | 01 |  0.0122 |  0.0388\n",
      "00:42:41 08 | 02 |  0.0064 |  0.0155\n",
      "00:42:52 08 | 03 |  0.0270 |  0.0146\n",
      "00:43:03 08 | 04 |  0.0305 |  0.0391\n",
      "00:43:14 08 | 05 |  0.0250 |  0.0188\n",
      "00:43:25 08 | 06 |  0.0214 |  0.0252\n",
      "00:43:36 08 | 07 |  0.0216 |  0.0401\n",
      "00:43:47 08 | 08 |  0.0266 |  0.0251\n",
      "00:43:58 08 | 09 |  0.0222 |  0.0172\n",
      "00:44:09 08 | 10 |  0.0244 |  0.0241\n",
      "00:44:20 08 | 11 |  0.0114 |  0.0250\n",
      "00:44:31 08 | 12 |  0.0266 |  0.0171\n",
      "00:44:42 08 | 13 |  0.0156 |  0.0072\n",
      "00:44:53 08 | 14 |  0.0120 |  0.0053\n",
      "00:45:03 08 | 15 |  0.0047 | -0.0077\n",
      "00:45:14 08 | 16 | -0.0022 | -0.0047\n",
      "00:45:24 08 | 17 |  0.0127 |  0.0031\n",
      "00:45:35 08 | 18 |  0.0079 | -0.0067\n",
      "00:45:46 08 | 19 |  0.0172 |  0.0054\n",
      "00:45:56 08 | 20 |  0.0173 |  0.0264\n",
      "00:46:08 09 | 01 |  0.0058 |  0.0176\n",
      "00:46:18 09 | 02 |  0.0036 |  0.0267\n",
      "00:46:29 09 | 03 | -0.0006 | -0.0282\n",
      "00:46:40 09 | 04 |  0.0088 | -0.0056\n",
      "00:46:51 09 | 05 |  0.0032 |  0.0046\n",
      "00:47:01 09 | 06 |  0.0071 |  0.0145\n",
      "00:47:12 09 | 07 |  0.0078 | -0.0027\n",
      "00:47:23 09 | 08 |  0.0015 |  0.0068\n",
      "00:47:33 09 | 09 | -0.0003 |  0.0124\n",
      "00:47:45 09 | 10 |  0.0226 |  0.0289\n",
      "00:47:56 09 | 11 |  0.0101 |  0.0261\n",
      "00:48:07 09 | 12 |  0.0236 |  0.0203\n",
      "00:48:18 09 | 13 |  0.0040 | -0.0173\n",
      "00:48:29 09 | 14 |  0.0141 |  0.0169\n",
      "00:48:40 09 | 15 |  0.0084 |  0.0009\n",
      "00:48:51 09 | 16 |  0.0020 | -0.0061\n",
      "00:49:02 09 | 17 |  0.0244 |  0.0155\n",
      "00:49:13 09 | 18 |  0.0087 |  0.0161\n",
      "00:49:24 09 | 19 |  0.0082 | -0.0017\n",
      "00:49:34 09 | 20 |  0.0086 |  0.0152\n",
      "00:49:46 10 | 01 |  0.0397 |  0.0269\n",
      "00:49:56 10 | 02 | -0.0380 | -0.0525\n",
      "00:50:07 10 | 03 | -0.0174 | -0.0414\n",
      "00:50:18 10 | 04 | -0.0250 | -0.0543\n",
      "00:50:29 10 | 05 | -0.0110 | -0.0330\n",
      "00:50:39 10 | 06 | -0.0114 | -0.0147\n",
      "00:50:50 10 | 07 | -0.0122 | -0.0204\n",
      "00:51:01 10 | 08 | -0.0058 | -0.0047\n",
      "00:51:12 10 | 09 | -0.0068 |  0.0029\n",
      "00:51:22 10 | 10 | -0.0128 | -0.0028\n",
      "00:51:33 10 | 11 | -0.0192 | -0.0125\n",
      "00:51:44 10 | 12 | -0.0133 | -0.0037\n",
      "00:51:55 10 | 13 | -0.0150 | -0.0137\n",
      "00:52:06 10 | 14 |  0.0132 |  0.0136\n",
      "00:52:17 10 | 15 | -0.0037 |  0.0064\n",
      "00:52:29 10 | 16 |  0.0016 |  0.0145\n",
      "00:52:40 10 | 17 | -0.0054 | -0.0024\n",
      "00:52:51 10 | 18 |  0.0124 |  0.0225\n",
      "00:53:02 10 | 19 | -0.0226 | -0.0119\n",
      "00:53:14 10 | 20 | -0.0118 | -0.0098\n",
      "00:53:25 11 | 01 |  0.0277 |  0.0221\n",
      "00:53:36 11 | 02 |  0.0219 |  0.0191\n",
      "00:53:47 11 | 03 |  0.0232 |  0.0295\n",
      "00:53:58 11 | 04 |  0.0103 | -0.0028\n",
      "00:54:09 11 | 05 |  0.0220 |  0.0130\n",
      "00:54:20 11 | 06 |  0.0196 |  0.0161\n",
      "00:54:32 11 | 07 |  0.0227 |  0.0264\n",
      "00:54:44 11 | 08 |  0.0176 |  0.0160\n",
      "00:54:56 11 | 09 |  0.0301 |  0.0290\n",
      "00:55:07 11 | 10 |  0.0207 |  0.0212\n",
      "00:55:18 11 | 11 |  0.0089 |  0.0059\n",
      "00:55:29 11 | 12 |  0.0001 |  0.0084\n",
      "00:55:40 11 | 13 |  0.0020 |  0.0013\n",
      "00:55:52 11 | 14 |  0.0057 |  0.0084\n",
      "00:56:03 11 | 15 |  0.0043 |  0.0128\n",
      "00:56:15 11 | 16 |  0.0181 |  0.0167\n",
      "00:56:26 11 | 17 |  0.0010 | -0.0090\n",
      "00:56:38 11 | 18 |  0.0040 |  0.0075\n",
      "00:56:50 11 | 19 | -0.0115 | -0.0048\n",
      "00:57:02 11 | 20 |  0.0177 |  0.0197\n",
      "00:57:13 12 | 01 | -0.0009 |  0.0026\n",
      "00:57:25 12 | 02 |  0.0185 |  0.0211\n",
      "00:57:36 12 | 03 |  0.0017 |  0.0006\n",
      "00:57:48 12 | 04 | -0.0068 | -0.0201\n",
      "00:57:59 12 | 05 |  0.0021 | -0.0010\n",
      "00:58:12 12 | 06 | -0.0015 |  0.0030\n",
      "00:58:25 12 | 07 | -0.0036 | -0.0216\n",
      "00:58:38 12 | 08 |  0.0029 | -0.0057\n",
      "00:58:50 12 | 09 | -0.0054 |  0.0035\n",
      "00:59:03 12 | 10 | -0.0088 | -0.0241\n",
      "00:59:15 12 | 11 | -0.0176 | -0.0288\n",
      "00:59:27 12 | 12 |  0.0022 |  0.0024\n",
      "00:59:39 12 | 13 | -0.0045 | -0.0263\n",
      "00:59:52 12 | 14 | -0.0040 |  0.0048\n",
      "01:00:04 12 | 15 | -0.0009 | -0.0267\n",
      "01:00:17 12 | 16 | -0.0113 | -0.0091\n",
      "01:00:28 12 | 17 | -0.0096 | -0.0270\n",
      "01:00:39 12 | 18 | -0.0062 | -0.0097\n",
      "01:00:50 12 | 19 | -0.0084 | -0.0084\n",
      "01:01:02 12 | 20 | -0.0072 | -0.0076\n",
      "(16, 8) tanh 0.2 256\n",
      "00:00:04 01 | 01 |  0.0025 | -0.0032\n",
      "00:00:08 01 | 02 |  0.0025 | -0.0059\n",
      "00:00:11 01 | 03 |  0.0087 |  0.0190\n",
      "00:00:15 01 | 04 |  0.0052 |  0.0028\n",
      "00:00:18 01 | 05 |  0.0080 |  0.0184\n",
      "00:00:21 01 | 06 |  0.0204 |  0.0137\n",
      "00:00:25 01 | 07 |  0.0072 |  0.0017\n",
      "00:00:28 01 | 08 | -0.0058 | -0.0155\n",
      "00:00:31 01 | 09 |  0.0090 | -0.0072\n",
      "00:00:35 01 | 10 |  0.0167 |  0.0169\n",
      "00:00:38 01 | 11 |  0.0093 |  0.0120\n",
      "00:00:41 01 | 12 |  0.0187 |  0.0148\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:00:45 01 | 13 |  0.0122 |  0.0047\n",
      "00:00:48 01 | 14 |  0.0132 |  0.0103\n",
      "00:00:52 01 | 15 |  0.0189 |  0.0105\n",
      "00:00:55 01 | 16 |  0.0219 |  0.0182\n",
      "00:00:58 01 | 17 |  0.0072 |  0.0102\n",
      "00:01:02 01 | 18 |  0.0174 |  0.0120\n",
      "00:01:05 01 | 19 |  0.0112 |  0.0205\n",
      "00:01:09 01 | 20 |  0.0226 |  0.0234\n",
      "00:01:13 02 | 01 | -0.0009 |  0.0010\n",
      "00:01:17 02 | 02 |  0.0182 |  0.0210\n",
      "00:01:20 02 | 03 |  0.0047 | -0.0160\n",
      "00:01:23 02 | 04 |  0.0095 |  0.0120\n",
      "00:01:27 02 | 05 | -0.0096 | -0.0121\n",
      "00:01:31 02 | 06 |  0.0128 |  0.0271\n",
      "00:01:34 02 | 07 |  0.0162 |  0.0223\n",
      "00:01:38 02 | 08 |  0.0120 |  0.0394\n",
      "00:01:41 02 | 09 |  0.0009 |  0.0115\n",
      "00:01:45 02 | 10 |  0.0159 |  0.0278\n",
      "00:01:48 02 | 11 |  0.0127 |  0.0252\n",
      "00:01:52 02 | 12 |  0.0241 |  0.0248\n",
      "00:01:56 02 | 13 |  0.0166 |  0.0191\n",
      "00:01:59 02 | 14 |  0.0124 |  0.0167\n",
      "00:02:02 02 | 15 |  0.0011 |  0.0133\n",
      "00:02:06 02 | 16 |  0.0041 |  0.0143\n",
      "00:02:09 02 | 17 |  0.0107 |  0.0084\n",
      "00:02:13 02 | 18 |  0.0001 |  0.0019\n",
      "00:02:16 02 | 19 |  0.0042 |  0.0044\n",
      "00:02:20 02 | 20 |  0.0237 |  0.0189\n",
      "00:02:24 03 | 01 |  0.0118 |  0.0109\n",
      "00:02:28 03 | 02 | -0.0222 | -0.0439\n",
      "00:02:31 03 | 03 | -0.0204 | -0.0229\n",
      "00:02:35 03 | 04 | -0.0128 | -0.0193\n",
      "00:02:39 03 | 05 | -0.0038 | -0.0007\n",
      "00:02:43 03 | 06 | -0.0090 | -0.0341\n",
      "00:02:46 03 | 07 | -0.0094 | -0.0023\n",
      "00:02:50 03 | 08 |  0.0179 |  0.0134\n",
      "00:02:53 03 | 09 |  0.0096 |  0.0107\n",
      "00:02:56 03 | 10 |  0.0218 |  0.0134\n",
      "00:02:60 03 | 11 |  0.0168 |  0.0178\n",
      "00:03:03 03 | 12 | -0.0078 | -0.0162\n",
      "00:03:06 03 | 13 |  0.0081 |  0.0027\n",
      "00:03:10 03 | 14 |  0.0080 |  0.0053\n",
      "00:03:13 03 | 15 |  0.0001 | -0.0053\n",
      "00:03:17 03 | 16 | -0.0046 | -0.0102\n",
      "00:03:20 03 | 17 |  0.0017 |  0.0065\n",
      "00:03:24 03 | 18 |  0.0013 |  0.0064\n",
      "00:03:27 03 | 19 |  0.0074 |  0.0088\n",
      "00:03:30 03 | 20 |  0.0006 | -0.0180\n",
      "00:03:35 04 | 01 |  0.0071 |  0.0060\n",
      "00:03:39 04 | 02 | -0.0175 |  0.0022\n",
      "00:03:43 04 | 03 | -0.0223 | -0.0243\n",
      "00:03:47 04 | 04 |  0.0030 |  0.0009\n",
      "00:03:50 04 | 05 | -0.0357 | -0.0384\n",
      "00:03:54 04 | 06 | -0.0090 | -0.0221\n",
      "00:03:57 04 | 07 |  0.0233 |  0.0196\n",
      "00:04:01 04 | 08 | -0.0118 | -0.0005\n",
      "00:04:04 04 | 09 | -0.0137 | -0.0387\n",
      "00:04:08 04 | 10 |  0.0028 | -0.0084\n",
      "00:04:11 04 | 11 |  0.0095 |  0.0104\n",
      "00:04:15 04 | 12 |  0.0140 | -0.0120\n",
      "00:04:18 04 | 13 |  0.0212 | -0.0019\n",
      "00:04:22 04 | 14 |  0.0208 | -0.0106\n",
      "00:04:25 04 | 15 |  0.0177 | -0.0168\n",
      "00:04:28 04 | 16 |  0.0135 |  0.0107\n",
      "00:04:32 04 | 17 |  0.0235 |  0.0047\n",
      "00:04:35 04 | 18 |  0.0221 |  0.0013\n",
      "00:04:39 04 | 19 |  0.0118 | -0.0056\n",
      "00:04:42 04 | 20 |  0.0258 |  0.0149\n",
      "00:04:46 05 | 01 | -0.0117 | -0.0006\n",
      "00:04:50 05 | 02 |  0.0075 |  0.0291\n",
      "00:04:53 05 | 03 | -0.0150 | -0.0038\n",
      "00:04:57 05 | 04 |  0.0140 |  0.0107\n",
      "00:05:00 05 | 05 |  0.0187 |  0.0235\n",
      "00:05:04 05 | 06 |  0.0203 |  0.0250\n",
      "00:05:07 05 | 07 |  0.0066 | -0.0035\n",
      "00:05:11 05 | 08 |  0.0036 | -0.0144\n",
      "00:05:14 05 | 09 |  0.0057 | -0.0147\n",
      "00:05:17 05 | 10 | -0.0020 | -0.0072\n",
      "00:05:21 05 | 11 | -0.0158 | -0.0248\n",
      "00:05:24 05 | 12 | -0.0073 |  0.0080\n",
      "00:05:28 05 | 13 | -0.0048 | -0.0238\n",
      "00:05:31 05 | 14 |  0.0095 |  0.0000\n",
      "00:05:35 05 | 15 |  0.0123 |  0.0364\n",
      "00:05:38 05 | 16 |  0.0086 |  0.0169\n",
      "00:05:42 05 | 17 |  0.0151 |  0.0160\n",
      "00:05:45 05 | 18 |  0.0097 |  0.0100\n",
      "00:05:49 05 | 19 |  0.0060 |  0.0298\n",
      "00:05:52 05 | 20 | -0.0001 | -0.0113\n",
      "00:05:56 06 | 01 |  0.0067 | -0.0056\n",
      "00:05:60 06 | 02 |  0.0018 |  0.0116\n",
      "00:06:03 06 | 03 |  0.0161 |  0.0161\n",
      "00:06:07 06 | 04 |  0.0328 |  0.0209\n",
      "00:06:10 06 | 05 |  0.0273 |  0.0303\n",
      "00:06:13 06 | 06 |  0.0306 |  0.0368\n",
      "00:06:17 06 | 07 |  0.0356 |  0.0389\n",
      "00:06:20 06 | 08 |  0.0494 |  0.0521\n",
      "00:06:24 06 | 09 |  0.0381 |  0.0368\n",
      "00:06:27 06 | 10 |  0.0387 |  0.0425\n",
      "00:06:31 06 | 11 |  0.0315 |  0.0236\n",
      "00:06:34 06 | 12 |  0.0216 |  0.0164\n",
      "00:06:37 06 | 13 |  0.0016 | -0.0093\n",
      "00:06:41 06 | 14 |  0.0213 |  0.0055\n",
      "00:06:44 06 | 15 | -0.0038 | -0.0160\n",
      "00:06:48 06 | 16 |  0.0238 |  0.0149\n",
      "00:06:51 06 | 17 |  0.0111 |  0.0121\n",
      "00:06:55 06 | 18 |  0.0454 |  0.0508\n",
      "00:06:58 06 | 19 |  0.0329 |  0.0438\n",
      "00:07:02 06 | 20 |  0.0196 |  0.0298\n",
      "00:07:06 07 | 01 |  0.0013 |  0.0017\n",
      "00:07:09 07 | 02 | -0.0366 | -0.0368\n",
      "00:07:13 07 | 03 | -0.0023 |  0.0276\n",
      "00:07:16 07 | 04 |  0.0243 |  0.0714\n",
      "00:07:20 07 | 05 | -0.0008 |  0.0372\n",
      "00:07:23 07 | 06 |  0.0143 |  0.0566\n",
      "00:07:27 07 | 07 |  0.0303 |  0.0254\n",
      "00:07:30 07 | 08 |  0.0248 |  0.0435\n",
      "00:07:33 07 | 09 | -0.0077 |  0.0102\n",
      "00:07:37 07 | 10 |  0.0108 |  0.0078\n",
      "00:07:40 07 | 11 |  0.0219 |  0.0252\n",
      "00:07:44 07 | 12 |  0.0315 |  0.0485\n",
      "00:07:47 07 | 13 |  0.0121 |  0.0374\n",
      "00:07:51 07 | 14 |  0.0334 |  0.0476\n",
      "00:07:54 07 | 15 |  0.0142 |  0.0438\n",
      "00:07:58 07 | 16 |  0.0230 |  0.0379\n",
      "00:08:01 07 | 17 |  0.0206 |  0.0316\n",
      "00:08:05 07 | 18 |  0.0316 |  0.0579\n",
      "00:08:08 07 | 19 |  0.0413 |  0.0465\n",
      "00:08:11 07 | 20 |  0.0134 |  0.0251\n",
      "00:08:16 08 | 01 | -0.0073 | -0.0154\n",
      "00:08:19 08 | 02 |  0.0018 | -0.0019\n",
      "00:08:22 08 | 03 |  0.0019 |  0.0111\n",
      "00:08:26 08 | 04 | -0.0029 |  0.0358\n",
      "00:08:29 08 | 05 |  0.0079 | -0.0067\n",
      "00:08:33 08 | 06 |  0.0056 | -0.0059\n",
      "00:08:36 08 | 07 |  0.0183 |  0.0201\n",
      "00:08:40 08 | 08 |  0.0112 |  0.0075\n",
      "00:08:43 08 | 09 |  0.0033 |  0.0285\n",
      "00:08:47 08 | 10 |  0.0042 |  0.0034\n",
      "00:08:50 08 | 11 |  0.0137 |  0.0306\n",
      "00:08:53 08 | 12 |  0.0020 |  0.0213\n",
      "00:08:57 08 | 13 |  0.0067 |  0.0192\n",
      "00:09:00 08 | 14 |  0.0134 |  0.0137\n",
      "00:09:04 08 | 15 |  0.0116 |  0.0115\n",
      "00:09:07 08 | 16 |  0.0242 |  0.0131\n",
      "00:09:11 08 | 17 |  0.0191 |  0.0206\n",
      "00:09:14 08 | 18 |  0.0143 |  0.0112\n",
      "00:09:17 08 | 19 |  0.0088 |  0.0160\n",
      "00:09:20 08 | 20 |  0.0179 |  0.0031\n",
      "00:09:24 09 | 01 |  0.0108 |  0.0166\n",
      "00:09:27 09 | 02 |  0.0117 |  0.0285\n",
      "00:09:31 09 | 03 |  0.0033 |  0.0055\n",
      "00:09:34 09 | 04 |  0.0024 |  0.0068\n",
      "00:09:37 09 | 05 |  0.0170 | -0.0091\n",
      "00:09:40 09 | 06 |  0.0113 |  0.0120\n",
      "00:09:43 09 | 07 |  0.0218 |  0.0179\n",
      "00:09:46 09 | 08 |  0.0143 |  0.0045\n",
      "00:09:49 09 | 09 |  0.0210 |  0.0168\n",
      "00:09:52 09 | 10 |  0.0156 |  0.0099\n",
      "00:09:55 09 | 11 |  0.0118 |  0.0033\n",
      "00:09:59 09 | 12 |  0.0106 |  0.0014\n",
      "00:10:02 09 | 13 |  0.0039 |  0.0040\n",
      "00:10:05 09 | 14 |  0.0054 |  0.0084\n",
      "00:10:08 09 | 15 |  0.0151 |  0.0068\n",
      "00:10:11 09 | 16 |  0.0119 |  0.0122\n",
      "00:10:14 09 | 17 |  0.0198 |  0.0240\n",
      "00:10:17 09 | 18 |  0.0152 |  0.0183\n",
      "00:10:20 09 | 19 |  0.0183 |  0.0224\n",
      "00:10:23 09 | 20 |  0.0277 |  0.0179\n",
      "00:10:27 10 | 01 |  0.0205 |  0.0197\n",
      "00:10:30 10 | 02 | -0.0252 | -0.0281\n",
      "00:10:33 10 | 03 | -0.0045 | -0.0215\n",
      "00:10:36 10 | 04 | -0.0227 | -0.0321\n",
      "00:10:40 10 | 05 |  0.0255 |  0.0172\n",
      "00:10:43 10 | 06 |  0.0013 |  0.0130\n",
      "00:10:46 10 | 07 |  0.0068 |  0.0172\n",
      "00:10:49 10 | 08 | -0.0211 | -0.0358\n",
      "00:10:52 10 | 09 | -0.0012 |  0.0111\n",
      "00:10:55 10 | 10 | -0.0247 | -0.0138\n",
      "00:10:58 10 | 11 |  0.0083 |  0.0154\n",
      "00:11:01 10 | 12 | -0.0263 | -0.0493\n",
      "00:11:05 10 | 13 | -0.0180 | -0.0224\n",
      "00:11:08 10 | 14 | -0.0151 |  0.0067\n",
      "00:11:11 10 | 15 | -0.0206 | -0.0469\n",
      "00:11:14 10 | 16 | -0.0331 | -0.0463\n",
      "00:11:17 10 | 17 | -0.0460 | -0.0505\n",
      "00:11:20 10 | 18 | -0.0161 | -0.0118\n",
      "00:11:23 10 | 19 | -0.0135 | -0.0173\n",
      "00:11:27 10 | 20 |  0.0030 |  0.0042\n",
      "00:11:30 11 | 01 | -0.0023 | -0.0079\n",
      "00:11:34 11 | 02 |  0.0053 | -0.0014\n",
      "00:11:37 11 | 03 |  0.0186 |  0.0325\n",
      "00:11:40 11 | 04 |  0.0218 |  0.0160\n",
      "00:11:43 11 | 05 |  0.0210 |  0.0190\n",
      "00:11:46 11 | 06 |  0.0182 |  0.0137\n",
      "00:11:49 11 | 07 |  0.0317 |  0.0216\n",
      "00:11:52 11 | 08 |  0.0280 |  0.0273\n",
      "00:11:55 11 | 09 |  0.0320 |  0.0438\n",
      "00:11:59 11 | 10 |  0.0213 |  0.0041\n",
      "00:12:02 11 | 11 |  0.0168 |  0.0088\n",
      "00:12:05 11 | 12 |  0.0190 |  0.0177\n",
      "00:12:08 11 | 13 |  0.0013 | -0.0006\n",
      "00:12:11 11 | 14 |  0.0062 | -0.0114\n",
      "00:12:14 11 | 15 |  0.0148 |  0.0155\n",
      "00:12:17 11 | 16 |  0.0197 |  0.0226\n",
      "00:12:20 11 | 17 |  0.0050 |  0.0072\n",
      "00:12:24 11 | 18 |  0.0026 |  0.0024\n",
      "00:12:27 11 | 19 |  0.0166 |  0.0062\n",
      "00:12:30 11 | 20 |  0.0191 |  0.0242\n",
      "00:12:34 12 | 01 |  0.0027 |  0.0018\n",
      "00:12:37 12 | 02 |  0.0034 |  0.0059\n",
      "00:12:40 12 | 03 |  0.0081 | -0.0128\n",
      "00:12:43 12 | 04 | -0.0003 |  0.0109\n",
      "00:12:46 12 | 05 | -0.0050 | -0.0093\n",
      "00:12:49 12 | 06 | -0.0088 | -0.0075\n",
      "00:12:53 12 | 07 | -0.0071 | -0.0113\n",
      "00:12:56 12 | 08 | -0.0145 | -0.0063\n",
      "00:12:59 12 | 09 | -0.0211 | -0.0211\n",
      "00:13:02 12 | 10 | -0.0099 | -0.0040\n",
      "00:13:05 12 | 11 | -0.0139 | -0.0116\n",
      "00:13:08 12 | 12 | -0.0097 | -0.0136\n",
      "00:13:11 12 | 13 | -0.0117 | -0.0033\n",
      "00:13:14 12 | 14 | -0.0143 | -0.0202\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:13:18 12 | 15 | -0.0099 |  0.0021\n",
      "00:13:21 12 | 16 | -0.0099 | -0.0002\n",
      "00:13:24 12 | 17 | -0.0186 | -0.0183\n",
      "00:13:27 12 | 18 | -0.0194 | -0.0257\n",
      "00:13:30 12 | 19 | -0.0070 |  0.0174\n",
      "00:13:33 12 | 20 | -0.0006 |  0.0061\n",
      "(16, 8) tanh 0.1 64\n",
      "00:00:11 01 | 01 | -0.0010 |  0.0107\n",
      "00:00:21 01 | 02 |  0.0094 |  0.0034\n",
      "00:00:31 01 | 03 |  0.0145 |  0.0105\n",
      "00:00:42 01 | 04 |  0.0079 |  0.0031\n",
      "00:00:52 01 | 05 | -0.0004 | -0.0106\n",
      "00:01:02 01 | 06 | -0.0012 | -0.0010\n",
      "00:01:12 01 | 07 |  0.0072 |  0.0093\n",
      "00:01:23 01 | 08 |  0.0038 |  0.0079\n",
      "00:01:33 01 | 09 |  0.0080 | -0.0035\n",
      "00:01:43 01 | 10 |  0.0146 |  0.0087\n",
      "00:01:54 01 | 11 |  0.0209 |  0.0189\n",
      "00:02:04 01 | 12 |  0.0151 |  0.0088\n",
      "00:02:14 01 | 13 |  0.0033 |  0.0089\n",
      "00:02:24 01 | 14 |  0.0237 |  0.0151\n",
      "00:02:35 01 | 15 |  0.0222 |  0.0094\n",
      "00:02:45 01 | 16 |  0.0170 |  0.0186\n",
      "00:02:55 01 | 17 |  0.0222 |  0.0206\n",
      "00:03:05 01 | 18 |  0.0170 |  0.0156\n",
      "00:03:16 01 | 19 |  0.0269 |  0.0236\n",
      "00:03:26 01 | 20 |  0.0214 |  0.0176\n",
      "00:03:37 02 | 01 | -0.0027 |  0.0125\n",
      "00:03:47 02 | 02 |  0.0161 |  0.0358\n",
      "00:03:58 02 | 03 |  0.0087 | -0.0096\n",
      "00:04:08 02 | 04 | -0.0003 |  0.0111\n",
      "00:04:18 02 | 05 | -0.0090 |  0.0015\n",
      "00:04:29 02 | 06 | -0.0027 | -0.0006\n",
      "00:04:39 02 | 07 |  0.0051 |  0.0081\n",
      "00:04:49 02 | 08 |  0.0067 | -0.0027\n",
      "00:04:59 02 | 09 | -0.0006 | -0.0048\n",
      "00:05:09 02 | 10 |  0.0010 | -0.0053\n",
      "00:05:20 02 | 11 | -0.0047 | -0.0069\n",
      "00:05:30 02 | 12 | -0.0073 |  0.0060\n",
      "00:05:40 02 | 13 | -0.0108 |  0.0007\n",
      "00:05:50 02 | 14 | -0.0089 | -0.0035\n",
      "00:06:01 02 | 15 |  0.0026 |  0.0020\n",
      "00:06:11 02 | 16 |  0.0076 |  0.0105\n",
      "00:06:21 02 | 17 | -0.0133 | -0.0112\n",
      "00:06:31 02 | 18 |  0.0017 |  0.0070\n",
      "00:06:42 02 | 19 | -0.0074 | -0.0030\n",
      "00:06:52 02 | 20 |  0.0085 |  0.0002\n",
      "00:07:03 03 | 01 | -0.0112 |  0.0072\n",
      "00:07:13 03 | 02 |  0.0002 |  0.0095\n",
      "00:07:23 03 | 03 | -0.0192 | -0.0243\n",
      "00:07:34 03 | 04 |  0.0137 |  0.0059\n",
      "00:07:44 03 | 05 | -0.0083 | -0.0205\n",
      "00:07:54 03 | 06 |  0.0076 | -0.0195\n",
      "00:08:05 03 | 07 | -0.0029 | -0.0126\n",
      "00:08:15 03 | 08 | -0.0125 | -0.0177\n",
      "00:08:26 03 | 09 | -0.0061 | -0.0114\n",
      "00:08:36 03 | 10 | -0.0046 | -0.0188\n",
      "00:08:46 03 | 11 |  0.0005 | -0.0084\n",
      "00:08:57 03 | 12 |  0.0093 | -0.0024\n",
      "00:09:07 03 | 13 | -0.0027 | -0.0150\n",
      "00:09:18 03 | 14 | -0.0009 | -0.0064\n",
      "00:09:28 03 | 15 |  0.0190 |  0.0208\n",
      "00:09:39 03 | 16 |  0.0045 |  0.0013\n",
      "00:09:49 03 | 17 |  0.0124 |  0.0061\n",
      "00:09:59 03 | 18 |  0.0131 |  0.0311\n",
      "00:10:10 03 | 19 |  0.0181 |  0.0235\n",
      "00:10:20 03 | 20 |  0.0265 |  0.0436\n",
      "00:10:31 04 | 01 | -0.0037 | -0.0040\n",
      "00:10:41 04 | 02 |  0.0124 |  0.0049\n",
      "00:10:52 04 | 03 | -0.0069 | -0.0281\n",
      "00:11:02 04 | 04 |  0.0204 |  0.0269\n",
      "00:11:12 04 | 05 | -0.0376 | -0.0399\n",
      "00:11:23 04 | 06 | -0.0128 | -0.0040\n",
      "00:11:33 04 | 07 |  0.0055 |  0.0160\n",
      "00:11:43 04 | 08 |  0.0081 |  0.0072\n",
      "00:11:54 04 | 09 | -0.0075 | -0.0083\n",
      "00:12:04 04 | 10 | -0.0216 | -0.0235\n",
      "00:12:14 04 | 11 | -0.0389 | -0.0386\n",
      "00:12:25 04 | 12 |  0.0044 |  0.0039\n",
      "00:12:35 04 | 13 | -0.0168 | -0.0107\n",
      "00:12:45 04 | 14 | -0.0130 | -0.0227\n",
      "00:12:55 04 | 15 | -0.0477 | -0.0523\n",
      "00:13:06 04 | 16 | -0.0203 |  0.0094\n",
      "00:13:16 04 | 17 | -0.0392 | -0.0385\n",
      "00:13:26 04 | 18 | -0.0089 |  0.0011\n",
      "00:13:37 04 | 19 | -0.0115 | -0.0060\n",
      "00:13:47 04 | 20 |  0.0142 |  0.0292\n",
      "00:13:58 05 | 01 |  0.0128 |  0.0013\n",
      "00:14:08 05 | 02 |  0.0070 |  0.0034\n",
      "00:14:18 05 | 03 |  0.0013 | -0.0105\n",
      "00:14:29 05 | 04 | -0.0077 | -0.0188\n",
      "00:14:39 05 | 05 |  0.0184 | -0.0044\n",
      "00:14:50 05 | 06 | -0.0069 |  0.0163\n",
      "00:15:00 05 | 07 |  0.0047 |  0.0349\n",
      "00:15:10 05 | 08 |  0.0139 |  0.0012\n",
      "00:15:21 05 | 09 | -0.0061 |  0.0039\n",
      "00:15:31 05 | 10 |  0.0216 |  0.0444\n",
      "00:15:41 05 | 11 |  0.0111 |  0.0044\n",
      "00:15:52 05 | 12 |  0.0035 |  0.0003\n",
      "00:16:02 05 | 13 |  0.0073 |  0.0104\n",
      "00:16:13 05 | 14 |  0.0021 | -0.0019\n",
      "00:16:23 05 | 15 | -0.0002 |  0.0063\n",
      "00:16:33 05 | 16 | -0.0110 | -0.0224\n",
      "00:16:44 05 | 17 |  0.0148 |  0.0183\n",
      "00:16:54 05 | 18 | -0.0178 | -0.0182\n",
      "00:17:05 05 | 19 | -0.0052 | -0.0061\n",
      "00:17:15 05 | 20 |  0.0030 |  0.0177\n",
      "00:17:27 06 | 01 |  0.0322 |  0.0244\n",
      "00:17:37 06 | 02 |  0.0321 |  0.0046\n",
      "00:17:47 06 | 03 |  0.0366 |  0.0276\n",
      "00:17:57 06 | 04 |  0.0258 |  0.0316\n",
      "00:18:08 06 | 05 |  0.0238 |  0.0177\n",
      "00:18:18 06 | 06 |  0.0450 |  0.0277\n",
      "00:18:28 06 | 07 |  0.0308 |  0.0531\n",
      "00:18:38 06 | 08 |  0.0254 |  0.0311\n",
      "00:18:49 06 | 09 |  0.0300 |  0.0371\n",
      "00:18:59 06 | 10 |  0.0498 |  0.0496\n",
      "00:19:09 06 | 11 |  0.0386 |  0.0441\n",
      "00:19:20 06 | 12 |  0.0259 |  0.0290\n",
      "00:19:30 06 | 13 |  0.0361 |  0.0319\n",
      "00:19:40 06 | 14 |  0.0276 |  0.0582\n",
      "00:19:50 06 | 15 |  0.0268 |  0.0385\n",
      "00:20:01 06 | 16 |  0.0224 |  0.0318\n",
      "00:20:11 06 | 17 |  0.0239 |  0.0497\n",
      "00:20:21 06 | 18 |  0.0187 |  0.0314\n",
      "00:20:32 06 | 19 |  0.0289 |  0.0489\n",
      "00:20:42 06 | 20 |  0.0320 |  0.0399\n",
      "00:20:53 07 | 01 |  0.0091 |  0.0540\n",
      "00:21:03 07 | 02 |  0.0277 |  0.0347\n",
      "00:21:13 07 | 03 |  0.0176 |  0.0199\n",
      "00:21:23 07 | 04 |  0.0271 |  0.0453\n",
      "00:21:34 07 | 05 |  0.0279 |  0.0583\n",
      "00:21:44 07 | 06 |  0.0359 |  0.0271\n",
      "00:21:54 07 | 07 |  0.0341 |  0.0160\n",
      "00:22:04 07 | 08 |  0.0163 | -0.0021\n",
      "00:22:15 07 | 09 |  0.0242 | -0.0000\n",
      "00:22:25 07 | 10 |  0.0321 |  0.0326\n",
      "00:22:35 07 | 11 |  0.0223 |  0.0163\n",
      "00:22:45 07 | 12 |  0.0264 |  0.0112\n",
      "00:22:56 07 | 13 |  0.0001 | -0.0160\n",
      "00:23:06 07 | 14 |  0.0085 |  0.0109\n",
      "00:23:16 07 | 15 |  0.0005 |  0.0076\n",
      "00:23:27 07 | 16 |  0.0048 |  0.0139\n",
      "00:23:37 07 | 17 |  0.0053 | -0.0018\n",
      "00:23:47 07 | 18 | -0.0005 | -0.0058\n",
      "00:23:57 07 | 19 | -0.0013 |  0.0036\n",
      "00:24:08 07 | 20 | -0.0081 | -0.0024\n",
      "00:24:19 08 | 01 |  0.0069 | -0.0027\n",
      "00:24:29 08 | 02 |  0.0020 | -0.0094\n",
      "00:24:39 08 | 03 |  0.0028 | -0.0131\n",
      "00:24:49 08 | 04 |  0.0133 |  0.0018\n",
      "00:24:60 08 | 05 |  0.0062 | -0.0111\n",
      "00:25:10 08 | 06 |  0.0214 |  0.0177\n",
      "00:25:20 08 | 07 |  0.0265 |  0.0321\n",
      "00:25:31 08 | 08 |  0.0149 |  0.0419\n",
      "00:25:41 08 | 09 | -0.0070 |  0.0054\n",
      "00:25:51 08 | 10 |  0.0052 |  0.0263\n",
      "00:26:01 08 | 11 |  0.0102 |  0.0101\n",
      "00:26:12 08 | 12 |  0.0181 |  0.0158\n",
      "00:26:22 08 | 13 |  0.0102 |  0.0329\n",
      "00:26:32 08 | 14 |  0.0027 |  0.0174\n",
      "00:26:43 08 | 15 |  0.0095 |  0.0180\n",
      "00:26:53 08 | 16 | -0.0002 |  0.0344\n",
      "00:27:03 08 | 17 |  0.0070 |  0.0063\n",
      "00:27:13 08 | 18 |  0.0085 |  0.0014\n",
      "00:27:24 08 | 19 |  0.0223 |  0.0228\n",
      "00:27:34 08 | 20 |  0.0216 |  0.0268\n",
      "00:27:45 09 | 01 | -0.0062 |  0.0037\n",
      "00:27:55 09 | 02 |  0.0083 |  0.0285\n",
      "00:28:06 09 | 03 |  0.0051 |  0.0149\n",
      "00:28:16 09 | 04 | -0.0012 |  0.0148\n",
      "00:28:27 09 | 05 |  0.0047 |  0.0090\n",
      "00:28:37 09 | 06 |  0.0113 |  0.0027\n",
      "00:28:47 09 | 07 |  0.0029 |  0.0170\n",
      "00:28:58 09 | 08 |  0.0022 |  0.0072\n",
      "00:29:08 09 | 09 |  0.0090 |  0.0063\n",
      "00:29:18 09 | 10 |  0.0030 | -0.0109\n",
      "00:29:29 09 | 11 |  0.0081 |  0.0009\n",
      "00:29:39 09 | 12 |  0.0084 |  0.0040\n",
      "00:29:50 09 | 13 |  0.0047 | -0.0032\n",
      "00:29:60 09 | 14 |  0.0116 |  0.0168\n",
      "00:30:10 09 | 15 |  0.0048 |  0.0012\n",
      "00:30:21 09 | 16 |  0.0029 | -0.0062\n",
      "00:30:31 09 | 17 | -0.0081 | -0.0151\n",
      "00:30:41 09 | 18 |  0.0026 | -0.0103\n",
      "00:30:52 09 | 19 | -0.0062 | -0.0144\n",
      "00:31:02 09 | 20 | -0.0073 | -0.0095\n",
      "00:31:13 10 | 01 | -0.0356 | -0.0657\n",
      "00:31:23 10 | 02 | -0.0162 | -0.0163\n",
      "00:31:34 10 | 03 | -0.0309 | -0.0412\n",
      "00:31:44 10 | 04 |  0.0079 |  0.0148\n",
      "00:31:54 10 | 05 |  0.0241 |  0.0075\n",
      "00:32:05 10 | 06 | -0.0236 |  0.0041\n",
      "00:32:15 10 | 07 | -0.0076 |  0.0126\n",
      "00:32:26 10 | 08 | -0.0283 | -0.0249\n",
      "00:32:36 10 | 09 |  0.0090 |  0.0068\n",
      "00:32:46 10 | 10 |  0.0147 |  0.0123\n",
      "00:32:57 10 | 11 |  0.0121 |  0.0155\n",
      "00:33:07 10 | 12 | -0.0233 | -0.0304\n",
      "00:33:17 10 | 13 | -0.0313 | -0.0417\n",
      "00:33:27 10 | 14 | -0.0364 | -0.0143\n",
      "00:33:38 10 | 15 | -0.0121 | -0.0007\n",
      "00:33:48 10 | 16 |  0.0097 |  0.0072\n",
      "00:33:58 10 | 17 | -0.0217 | -0.0203\n",
      "00:34:08 10 | 18 | -0.0280 | -0.0261\n",
      "00:34:19 10 | 19 | -0.0242 | -0.0264\n",
      "00:34:29 10 | 20 | -0.0262 | -0.0003\n",
      "00:34:40 11 | 01 | -0.0002 | -0.0028\n",
      "00:34:50 11 | 02 |  0.0460 |  0.0372\n",
      "00:35:01 11 | 03 |  0.0046 |  0.0002\n",
      "00:35:11 11 | 04 |  0.0383 |  0.0467\n",
      "00:35:21 11 | 05 |  0.0188 |  0.0113\n",
      "00:35:31 11 | 06 |  0.0229 |  0.0087\n",
      "00:35:42 11 | 07 |  0.0393 |  0.0356\n",
      "00:35:52 11 | 08 |  0.0459 |  0.0375\n",
      "00:36:02 11 | 09 |  0.0149 |  0.0068\n",
      "00:36:13 11 | 10 |  0.0319 |  0.0200\n",
      "00:36:23 11 | 11 |  0.0126 |  0.0023\n",
      "00:36:33 11 | 12 |  0.0260 |  0.0130\n",
      "00:36:44 11 | 13 |  0.0265 |  0.0259\n",
      "00:36:54 11 | 14 |  0.0303 |  0.0202\n",
      "00:37:04 11 | 15 |  0.0240 |  0.0175\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:37:15 11 | 16 |  0.0240 |  0.0113\n",
      "00:37:25 11 | 17 |  0.0241 |  0.0158\n",
      "00:37:36 11 | 18 |  0.0176 |  0.0220\n",
      "00:37:46 11 | 19 |  0.0350 |  0.0285\n",
      "00:37:56 11 | 20 |  0.0286 |  0.0270\n",
      "00:38:07 12 | 01 |  0.0347 |  0.0331\n",
      "00:38:18 12 | 02 |  0.0240 |  0.0194\n",
      "00:38:28 12 | 03 |  0.0078 |  0.0189\n",
      "00:38:38 12 | 04 | -0.0009 |  0.0131\n",
      "00:38:48 12 | 05 | -0.0144 |  0.0023\n",
      "00:38:59 12 | 06 | -0.0050 | -0.0044\n",
      "00:39:09 12 | 07 | -0.0118 | -0.0199\n",
      "00:39:19 12 | 08 | -0.0081 | -0.0190\n",
      "00:39:29 12 | 09 | -0.0074 | -0.0020\n",
      "00:39:40 12 | 10 | -0.0077 | -0.0139\n",
      "00:39:50 12 | 11 | -0.0126 | -0.0102\n",
      "00:40:00 12 | 12 | -0.0131 | -0.0011\n",
      "00:40:11 12 | 13 | -0.0151 | -0.0225\n",
      "00:40:21 12 | 14 | -0.0144 | -0.0250\n",
      "00:40:31 12 | 15 | -0.0113 | -0.0240\n",
      "00:40:42 12 | 16 | -0.0084 | -0.0036\n",
      "00:40:52 12 | 17 | -0.0198 | -0.0180\n",
      "00:41:02 12 | 18 | -0.0223 | -0.0299\n",
      "00:41:12 12 | 19 | -0.0273 | -0.0348\n",
      "00:41:23 12 | 20 | -0.0234 | -0.0276\n",
      "(16, 8) tanh 0.1 256\n",
      "00:00:04 01 | 01 |  0.0031 | -0.0050\n",
      "00:00:07 01 | 02 | -0.0319 | -0.0224\n",
      "00:00:10 01 | 03 |  0.0235 |  0.0223\n",
      "00:00:13 01 | 04 | -0.0076 | -0.0030\n",
      "00:00:16 01 | 05 |  0.0130 |  0.0091\n",
      "00:00:19 01 | 06 |  0.0212 |  0.0181\n",
      "00:00:22 01 | 07 |  0.0253 |  0.0284\n",
      "00:00:26 01 | 08 |  0.0149 |  0.0156\n",
      "00:00:29 01 | 09 |  0.0205 |  0.0130\n",
      "00:00:32 01 | 10 |  0.0149 | -0.0052\n",
      "00:00:35 01 | 11 |  0.0183 |  0.0141\n",
      "00:00:38 01 | 12 |  0.0177 |  0.0203\n",
      "00:00:41 01 | 13 |  0.0168 |  0.0179\n",
      "00:00:44 01 | 14 |  0.0153 |  0.0222\n",
      "00:00:47 01 | 15 |  0.0127 |  0.0070\n",
      "00:00:51 01 | 16 | -0.0049 | -0.0025\n",
      "00:00:54 01 | 17 |  0.0038 | -0.0018\n",
      "00:00:57 01 | 18 |  0.0027 |  0.0045\n",
      "00:00:60 01 | 19 |  0.0083 | -0.0032\n",
      "00:01:03 01 | 20 |  0.0038 |  0.0143\n",
      "00:01:07 02 | 01 | -0.0197 | -0.0110\n",
      "00:01:10 02 | 02 |  0.0145 |  0.0181\n",
      "00:01:13 02 | 03 | -0.0087 |  0.0059\n",
      "00:01:16 02 | 04 |  0.0057 |  0.0350\n",
      "00:01:19 02 | 05 | -0.0005 |  0.0094\n",
      "00:01:22 02 | 06 |  0.0100 |  0.0083\n",
      "00:01:25 02 | 07 |  0.0086 |  0.0190\n",
      "00:01:29 02 | 08 |  0.0173 |  0.0248\n",
      "00:01:32 02 | 09 |  0.0095 |  0.0125\n",
      "00:01:35 02 | 10 | -0.0043 | -0.0034\n",
      "00:01:38 02 | 11 |  0.0101 | -0.0036\n",
      "00:01:41 02 | 12 | -0.0016 | -0.0099\n",
      "00:01:44 02 | 13 | -0.0051 | -0.0071\n",
      "00:01:47 02 | 14 |  0.0126 |  0.0246\n",
      "00:01:50 02 | 15 |  0.0097 |  0.0127\n",
      "00:01:53 02 | 16 |  0.0112 |  0.0179\n",
      "00:01:57 02 | 17 |  0.0105 |  0.0114\n",
      "00:01:60 02 | 18 |  0.0035 |  0.0106\n",
      "00:02:03 02 | 19 | -0.0076 |  0.0130\n",
      "00:02:06 02 | 20 |  0.0042 |  0.0086\n",
      "00:02:10 03 | 01 |  0.0058 |  0.0003\n",
      "00:02:13 03 | 02 | -0.0207 | -0.0168\n",
      "00:02:16 03 | 03 |  0.0049 |  0.0181\n",
      "00:02:19 03 | 04 | -0.0224 | -0.0236\n",
      "00:02:22 03 | 05 |  0.0081 |  0.0061\n",
      "00:02:25 03 | 06 |  0.0104 |  0.0202\n",
      "00:02:29 03 | 07 | -0.0017 |  0.0193\n",
      "00:02:32 03 | 08 | -0.0094 | -0.0256\n",
      "00:02:35 03 | 09 | -0.0122 | -0.0236\n",
      "00:02:38 03 | 10 | -0.0065 |  0.0047\n",
      "00:02:41 03 | 11 | -0.0201 | -0.0237\n",
      "00:02:44 03 | 12 | -0.0220 | -0.0336\n",
      "00:02:47 03 | 13 | -0.0034 | -0.0074\n",
      "00:02:50 03 | 14 |  0.0106 |  0.0241\n",
      "00:02:54 03 | 15 |  0.0125 |  0.0200\n",
      "00:02:57 03 | 16 | -0.0023 | -0.0157\n",
      "00:02:60 03 | 17 |  0.0087 |  0.0156\n",
      "00:03:03 03 | 18 |  0.0022 |  0.0094\n",
      "00:03:06 03 | 19 | -0.0015 | -0.0053\n",
      "00:03:09 03 | 20 |  0.0011 |  0.0069\n",
      "00:03:13 04 | 01 |  0.0034 | -0.0110\n",
      "00:03:16 04 | 02 |  0.0167 |  0.0123\n",
      "00:03:20 04 | 03 | -0.0068 | -0.0043\n",
      "00:03:23 04 | 04 |  0.0184 |  0.0031\n",
      "00:03:26 04 | 05 |  0.0190 |  0.0160\n",
      "00:03:29 04 | 06 | -0.0379 | -0.0372\n",
      "00:03:32 04 | 07 |  0.0062 |  0.0184\n",
      "00:03:35 04 | 08 |  0.0076 |  0.0062\n",
      "00:03:38 04 | 09 |  0.0110 |  0.0030\n",
      "00:03:41 04 | 10 |  0.0083 |  0.0001\n",
      "00:03:45 04 | 11 | -0.0078 | -0.0225\n",
      "00:03:48 04 | 12 | -0.0193 | -0.0172\n",
      "00:03:51 04 | 13 |  0.0019 |  0.0125\n",
      "00:03:54 04 | 14 |  0.0009 |  0.0218\n",
      "00:03:57 04 | 15 | -0.0078 | -0.0005\n",
      "00:04:00 04 | 16 | -0.0024 |  0.0072\n",
      "00:04:03 04 | 17 | -0.0091 | -0.0047\n",
      "00:04:07 04 | 18 | -0.0171 | -0.0085\n",
      "00:04:10 04 | 19 | -0.0021 | -0.0046\n",
      "00:04:13 04 | 20 | -0.0275 | -0.0442\n",
      "00:04:17 05 | 01 | -0.0051 | -0.0085\n",
      "00:04:20 05 | 02 |  0.0230 |  0.0323\n",
      "00:04:23 05 | 03 |  0.0290 |  0.0224\n",
      "00:04:26 05 | 04 |  0.0152 |  0.0167\n",
      "00:04:29 05 | 05 |  0.0067 |  0.0073\n",
      "00:04:32 05 | 06 |  0.0004 |  0.0053\n",
      "00:04:36 05 | 07 |  0.0133 |  0.0034\n",
      "00:04:39 05 | 08 |  0.0145 | -0.0039\n",
      "00:04:42 05 | 09 |  0.0008 |  0.0021\n",
      "00:04:45 05 | 10 |  0.0177 |  0.0140\n",
      "00:04:48 05 | 11 |  0.0169 |  0.0104\n",
      "00:04:51 05 | 12 |  0.0166 |  0.0055\n",
      "00:04:54 05 | 13 |  0.0164 |  0.0263\n",
      "00:04:58 05 | 14 |  0.0226 |  0.0186\n",
      "00:05:01 05 | 15 |  0.0310 |  0.0226\n",
      "00:05:04 05 | 16 |  0.0183 |  0.0136\n",
      "00:05:07 05 | 17 |  0.0169 |  0.0172\n",
      "00:05:10 05 | 18 |  0.0208 |  0.0208\n",
      "00:05:13 05 | 19 |  0.0142 |  0.0079\n",
      "00:05:16 05 | 20 |  0.0123 |  0.0118\n",
      "00:05:20 06 | 01 |  0.0087 | -0.0038\n",
      "00:05:23 06 | 02 |  0.0171 |  0.0061\n",
      "00:05:26 06 | 03 |  0.0382 |  0.0654\n",
      "00:05:30 06 | 04 |  0.0258 |  0.0216\n",
      "00:05:33 06 | 05 |  0.0229 |  0.0219\n",
      "00:05:36 06 | 06 |  0.0419 |  0.0362\n",
      "00:05:39 06 | 07 |  0.0454 |  0.0472\n",
      "00:05:42 06 | 08 |  0.0386 |  0.0347\n",
      "00:05:45 06 | 09 |  0.0501 |  0.0516\n",
      "00:05:48 06 | 10 |  0.0407 |  0.0373\n",
      "00:05:51 06 | 11 |  0.0410 |  0.0141\n",
      "00:05:54 06 | 12 |  0.0274 |  0.0305\n",
      "00:05:57 06 | 13 |  0.0245 |  0.0191\n",
      "00:05:60 06 | 14 |  0.0400 |  0.0441\n",
      "00:06:03 06 | 15 |  0.0341 |  0.0398\n",
      "00:06:06 06 | 16 |  0.0366 |  0.0566\n",
      "00:06:09 06 | 17 |  0.0333 |  0.0193\n",
      "00:06:12 06 | 18 |  0.0389 |  0.0497\n",
      "00:06:15 06 | 19 |  0.0489 |  0.0559\n",
      "00:06:18 06 | 20 |  0.0340 |  0.0598\n",
      "00:06:21 07 | 01 |  0.0204 |  0.0282\n",
      "00:06:24 07 | 02 |  0.0109 |  0.0223\n",
      "00:06:27 07 | 03 |  0.0193 |  0.0325\n",
      "00:06:30 07 | 04 |  0.0021 |  0.0040\n",
      "00:06:33 07 | 05 |  0.0215 |  0.0441\n",
      "00:06:36 07 | 06 |  0.0135 |  0.0233\n",
      "00:06:39 07 | 07 |  0.0186 |  0.0318\n",
      "00:06:42 07 | 08 |  0.0076 |  0.0177\n",
      "00:06:45 07 | 09 |  0.0167 |  0.0181\n",
      "00:06:48 07 | 10 |  0.0061 |  0.0304\n",
      "00:06:51 07 | 11 |  0.0125 |  0.0248\n",
      "00:06:54 07 | 12 |  0.0346 |  0.0295\n",
      "00:06:57 07 | 13 |  0.0210 |  0.0299\n",
      "00:07:00 07 | 14 |  0.0378 |  0.0537\n",
      "00:07:03 07 | 15 |  0.0338 |  0.0165\n",
      "00:07:06 07 | 16 |  0.0219 |  0.0193\n",
      "00:07:09 07 | 17 |  0.0397 |  0.0200\n",
      "00:07:12 07 | 18 |  0.0383 |  0.0191\n",
      "00:07:15 07 | 19 |  0.0489 |  0.0334\n",
      "00:07:18 07 | 20 |  0.0282 | -0.0038\n",
      "00:07:22 08 | 01 | -0.0089 |  0.0005\n",
      "00:07:25 08 | 02 | -0.0008 | -0.0042\n",
      "00:07:28 08 | 03 |  0.0160 |  0.0055\n",
      "00:07:31 08 | 04 | -0.0135 | -0.0251\n",
      "00:07:34 08 | 05 |  0.0125 |  0.0045\n",
      "00:07:37 08 | 06 |  0.0123 |  0.0315\n",
      "00:07:40 08 | 07 |  0.0261 |  0.0341\n",
      "00:07:43 08 | 08 |  0.0371 |  0.0469\n",
      "00:07:46 08 | 09 |  0.0190 |  0.0309\n",
      "00:07:49 08 | 10 |  0.0191 |  0.0159\n",
      "00:07:52 08 | 11 |  0.0129 |  0.0147\n",
      "00:07:55 08 | 12 |  0.0075 | -0.0096\n",
      "00:07:58 08 | 13 |  0.0104 |  0.0155\n",
      "00:08:01 08 | 14 |  0.0137 |  0.0059\n",
      "00:08:03 08 | 15 |  0.0111 | -0.0027\n",
      "00:08:06 08 | 16 |  0.0089 |  0.0054\n",
      "00:08:09 08 | 17 |  0.0239 |  0.0029\n",
      "00:08:12 08 | 18 |  0.0191 |  0.0054\n",
      "00:08:15 08 | 19 |  0.0208 | -0.0051\n",
      "00:08:18 08 | 20 |  0.0221 |  0.0128\n",
      "00:08:22 09 | 01 | -0.0032 |  0.0022\n",
      "00:08:25 09 | 02 | -0.0123 |  0.0100\n",
      "00:08:28 09 | 03 | -0.0096 |  0.0036\n",
      "00:08:31 09 | 04 | -0.0115 | -0.0161\n",
      "00:08:34 09 | 05 |  0.0113 |  0.0190\n",
      "00:08:37 09 | 06 |  0.0251 |  0.0203\n",
      "00:08:40 09 | 07 |  0.0062 | -0.0013\n",
      "00:08:43 09 | 08 | -0.0061 | -0.0054\n",
      "00:08:46 09 | 09 | -0.0095 |  0.0070\n",
      "00:08:49 09 | 10 |  0.0070 |  0.0007\n",
      "00:08:52 09 | 11 |  0.0011 |  0.0162\n",
      "00:08:55 09 | 12 |  0.0037 |  0.0050\n",
      "00:08:58 09 | 13 |  0.0027 |  0.0031\n",
      "00:09:02 09 | 14 | -0.0009 |  0.0126\n",
      "00:09:05 09 | 15 |  0.0132 |  0.0119\n",
      "00:09:08 09 | 16 |  0.0060 |  0.0142\n",
      "00:09:11 09 | 17 |  0.0059 |  0.0309\n",
      "00:09:14 09 | 18 |  0.0072 |  0.0165\n",
      "00:09:17 09 | 19 |  0.0081 |  0.0105\n",
      "00:09:20 09 | 20 |  0.0107 |  0.0330\n",
      "00:09:23 10 | 01 |  0.0382 |  0.0418\n",
      "00:09:26 10 | 02 |  0.0457 |  0.0393\n",
      "00:09:29 10 | 03 |  0.0032 | -0.0011\n",
      "00:09:32 10 | 04 |  0.0030 |  0.0095\n",
      "00:09:35 10 | 05 | -0.0112 | -0.0150\n",
      "00:09:38 10 | 06 | -0.0254 | -0.0331\n",
      "00:09:41 10 | 07 | -0.0319 | -0.0389\n",
      "00:09:44 10 | 08 |  0.0141 |  0.0165\n",
      "00:09:47 10 | 09 | -0.0119 | -0.0183\n",
      "00:09:50 10 | 10 | -0.0487 | -0.0535\n",
      "00:09:53 10 | 11 | -0.0315 | -0.0225\n",
      "00:09:56 10 | 12 | -0.0087 | -0.0021\n",
      "00:09:59 10 | 13 | -0.0127 | -0.0185\n",
      "00:10:02 10 | 14 | -0.0080 |  0.0020\n",
      "00:10:05 10 | 15 | -0.0091 | -0.0015\n",
      "00:10:08 10 | 16 | -0.0226 | -0.0191\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:10:11 10 | 17 |  0.0000 |  0.0021\n",
      "00:10:14 10 | 18 | -0.0086 |  0.0002\n",
      "00:10:17 10 | 19 | -0.0152 | -0.0202\n",
      "00:10:20 10 | 20 | -0.0006 |  0.0143\n",
      "00:10:24 11 | 01 |  0.0227 |  0.0295\n",
      "00:10:27 11 | 02 |  0.0362 |  0.0426\n",
      "00:10:30 11 | 03 |  0.0211 |  0.0192\n",
      "00:10:33 11 | 04 |  0.0218 |  0.0188\n",
      "00:10:36 11 | 05 |  0.0139 |  0.0120\n",
      "00:10:38 11 | 06 |  0.0349 |  0.0322\n",
      "00:10:42 11 | 07 | -0.0174 | -0.0129\n",
      "00:10:44 11 | 08 |  0.0354 |  0.0468\n",
      "00:10:47 11 | 09 |  0.0120 |  0.0117\n",
      "00:10:50 11 | 10 |  0.0157 |  0.0166\n",
      "00:10:53 11 | 11 |  0.0215 |  0.0212\n",
      "00:10:56 11 | 12 | -0.0112 | -0.0282\n",
      "00:10:59 11 | 13 |  0.0101 |  0.0010\n",
      "00:11:02 11 | 14 |  0.0102 |  0.0063\n",
      "00:11:05 11 | 15 |  0.0140 |  0.0119\n",
      "00:11:08 11 | 16 |  0.0157 |  0.0144\n",
      "00:11:11 11 | 17 |  0.0137 |  0.0086\n",
      "00:11:14 11 | 18 |  0.0026 | -0.0077\n",
      "00:11:17 11 | 19 |  0.0386 |  0.0374\n",
      "00:11:20 11 | 20 |  0.0275 |  0.0353\n",
      "00:11:24 12 | 01 |  0.0012 | -0.0324\n",
      "00:11:27 12 | 02 | -0.0092 | -0.0114\n",
      "00:11:30 12 | 03 |  0.0000 | -0.0120\n",
      "00:11:33 12 | 04 |  0.0260 |  0.0189\n",
      "00:11:36 12 | 05 |  0.0232 |  0.0022\n",
      "00:11:39 12 | 06 |  0.0158 |  0.0122\n",
      "00:11:42 12 | 07 |  0.0171 |  0.0050\n",
      "00:11:45 12 | 08 |  0.0034 | -0.0001\n",
      "00:11:48 12 | 09 |  0.0094 |  0.0050\n",
      "00:11:51 12 | 10 |  0.0118 |  0.0112\n",
      "00:11:54 12 | 11 |  0.0189 |  0.0336\n",
      "00:11:57 12 | 12 |  0.0222 |  0.0365\n",
      "00:11:60 12 | 13 |  0.0076 |  0.0172\n",
      "00:12:03 12 | 14 |  0.0134 |  0.0263\n",
      "00:12:06 12 | 15 |  0.0132 |  0.0162\n",
      "00:12:09 12 | 16 |  0.0142 |  0.0001\n",
      "00:12:12 12 | 17 |  0.0119 | -0.0199\n",
      "00:12:15 12 | 18 |  0.0081 | -0.0008\n",
      "00:12:18 12 | 19 |  0.0136 |  0.0163\n",
      "00:12:21 12 | 20 |  0.0141 |  0.0089\n",
      "(64, 32) tanh 0.2 64\n",
      "00:00:11 01 | 01 |  0.0025 |  0.0046\n",
      "00:00:21 01 | 02 | -0.0263 | -0.0167\n",
      "00:00:30 01 | 03 |  0.0099 |  0.0066\n",
      "00:00:40 01 | 04 | -0.0140 | -0.0092\n",
      "00:00:50 01 | 05 |  0.0200 |  0.0099\n",
      "00:01:00 01 | 06 |  0.0052 |  0.0161\n",
      "00:01:10 01 | 07 |  0.0213 |  0.0359\n",
      "00:01:20 01 | 08 |  0.0265 |  0.0174\n",
      "00:01:30 01 | 09 |  0.0126 |  0.0030\n",
      "00:01:39 01 | 10 |  0.0227 |  0.0205\n",
      "00:01:50 01 | 11 | -0.0061 |  0.0018\n",
      "00:01:59 01 | 12 |  0.0162 |  0.0034\n",
      "00:02:09 01 | 13 | -0.0005 | -0.0139\n",
      "00:02:19 01 | 14 | -0.0025 | -0.0076\n",
      "00:02:29 01 | 15 |  0.0171 |  0.0213\n",
      "00:02:39 01 | 16 | -0.0015 | -0.0009\n",
      "00:02:49 01 | 17 |  0.0064 |  0.0126\n",
      "00:02:59 01 | 18 |  0.0293 |  0.0383\n",
      "00:03:09 01 | 19 | -0.0123 |  0.0079\n",
      "00:03:18 01 | 20 |  0.0130 |  0.0088\n",
      "00:03:29 02 | 01 |  0.0083 |  0.0020\n",
      "00:03:39 02 | 02 |  0.0026 | -0.0067\n",
      "00:03:49 02 | 03 | -0.0265 | -0.0179\n",
      "00:03:59 02 | 04 | -0.0181 | -0.0114\n",
      "00:04:09 02 | 05 |  0.0254 |  0.0408\n",
      "00:04:19 02 | 06 | -0.0014 | -0.0066\n",
      "00:04:29 02 | 07 |  0.0055 |  0.0162\n",
      "00:04:39 02 | 08 |  0.0058 | -0.0025\n",
      "00:04:48 02 | 09 |  0.0035 | -0.0040\n",
      "00:04:58 02 | 10 | -0.0023 | -0.0024\n",
      "00:05:08 02 | 11 | -0.0012 |  0.0218\n",
      "00:05:18 02 | 12 | -0.0077 |  0.0032\n",
      "00:05:28 02 | 13 |  0.0020 |  0.0119\n",
      "00:05:38 02 | 14 | -0.0075 | -0.0018\n",
      "00:05:48 02 | 15 |  0.0048 | -0.0008\n",
      "00:05:58 02 | 16 | -0.0112 | -0.0071\n",
      "00:06:08 02 | 17 |  0.0004 |  0.0126\n",
      "00:06:18 02 | 18 |  0.0055 | -0.0021\n",
      "00:06:28 02 | 19 |  0.0083 |  0.0178\n",
      "00:06:38 02 | 20 | -0.0130 | -0.0049\n",
      "00:06:48 03 | 01 | -0.0084 |  0.0022\n",
      "00:06:58 03 | 02 |  0.0029 |  0.0026\n",
      "00:07:08 03 | 03 | -0.0010 |  0.0021\n",
      "00:07:18 03 | 04 | -0.0157 | -0.0279\n",
      "00:07:28 03 | 05 |  0.0266 |  0.0177\n",
      "00:07:38 03 | 06 |  0.0209 |  0.0460\n",
      "00:07:48 03 | 07 | -0.0088 | -0.0064\n",
      "00:07:58 03 | 08 |  0.0206 |  0.0461\n",
      "00:08:08 03 | 09 | -0.0146 | -0.0199\n",
      "00:08:18 03 | 10 |  0.0143 |  0.0362\n",
      "00:08:28 03 | 11 |  0.0190 |  0.0067\n",
      "00:08:38 03 | 12 | -0.0218 | -0.0212\n",
      "00:08:48 03 | 13 | -0.0080 | -0.0388\n",
      "00:08:58 03 | 14 |  0.0124 |  0.0277\n",
      "00:09:08 03 | 15 |  0.0326 |  0.0480\n",
      "00:09:18 03 | 16 | -0.0162 | -0.0536\n",
      "00:09:28 03 | 17 |  0.0062 |  0.0090\n",
      "00:09:38 03 | 18 | -0.0001 |  0.0095\n",
      "00:09:48 03 | 19 | -0.0062 | -0.0064\n",
      "00:09:57 03 | 20 | -0.0021 | -0.0081\n",
      "00:10:08 04 | 01 |  0.0237 |  0.0166\n",
      "00:10:18 04 | 02 | -0.0231 | -0.0156\n",
      "00:10:28 04 | 03 | -0.0267 | -0.0393\n",
      "00:10:38 04 | 04 | -0.0031 |  0.0025\n",
      "00:10:48 04 | 05 | -0.0194 | -0.0365\n",
      "00:10:58 04 | 06 |  0.0183 |  0.0083\n",
      "00:11:08 04 | 07 |  0.0133 |  0.0298\n",
      "00:11:18 04 | 08 |  0.0136 | -0.0075\n",
      "00:11:28 04 | 09 | -0.0218 | -0.0192\n",
      "00:11:38 04 | 10 | -0.0547 | -0.0417\n",
      "00:11:48 04 | 11 | -0.0196 | -0.0274\n",
      "00:11:58 04 | 12 | -0.0282 | -0.0294\n",
      "00:12:08 04 | 13 |  0.0277 |  0.0450\n",
      "00:12:18 04 | 14 | -0.0236 | -0.0313\n",
      "00:12:28 04 | 15 |  0.0104 |  0.0022\n",
      "00:12:38 04 | 16 | -0.0308 | -0.0633\n",
      "00:12:48 04 | 17 | -0.0132 | -0.0037\n",
      "00:12:58 04 | 18 | -0.0249 | -0.0187\n",
      "00:13:07 04 | 19 | -0.0310 | -0.0264\n",
      "00:13:17 04 | 20 |  0.0052 |  0.0229\n",
      "00:13:28 05 | 01 |  0.0016 | -0.0063\n",
      "00:13:38 05 | 02 |  0.0101 | -0.0001\n",
      "00:13:48 05 | 03 |  0.0108 | -0.0028\n",
      "00:13:58 05 | 04 | -0.0065 | -0.0125\n",
      "00:14:08 05 | 05 |  0.0085 |  0.0144\n",
      "00:14:18 05 | 06 |  0.0110 |  0.0109\n",
      "00:14:28 05 | 07 |  0.0090 |  0.0125\n",
      "00:14:38 05 | 08 | -0.0258 | -0.0145\n",
      "00:14:48 05 | 09 | -0.0166 | -0.0063\n",
      "00:14:58 05 | 10 |  0.0013 |  0.0121\n",
      "00:15:08 05 | 11 | -0.0088 | -0.0090\n",
      "00:15:17 05 | 12 | -0.0011 |  0.0103\n",
      "00:15:27 05 | 13 | -0.0160 | -0.0061\n",
      "00:15:37 05 | 14 | -0.0040 |  0.0005\n",
      "00:15:47 05 | 15 | -0.0098 | -0.0001\n",
      "00:15:57 05 | 16 | -0.0136 | -0.0032\n",
      "00:16:07 05 | 17 | -0.0065 | -0.0125\n",
      "00:16:17 05 | 18 | -0.0238 | -0.0128\n",
      "00:16:27 05 | 19 | -0.0155 | -0.0108\n",
      "00:16:37 05 | 20 | -0.0019 |  0.0113\n",
      "00:16:48 06 | 01 |  0.0145 |  0.0093\n",
      "00:16:58 06 | 02 |  0.0176 |  0.0365\n",
      "00:17:07 06 | 03 |  0.0024 |  0.0033\n",
      "00:17:17 06 | 04 |  0.0088 |  0.0085\n",
      "00:17:27 06 | 05 |  0.0124 |  0.0284\n",
      "00:17:37 06 | 06 |  0.0348 |  0.0405\n",
      "00:17:47 06 | 07 |  0.0345 |  0.0150\n",
      "00:17:57 06 | 08 |  0.0402 |  0.0291\n",
      "00:18:07 06 | 09 |  0.0460 |  0.0387\n",
      "00:18:17 06 | 10 |  0.0387 |  0.0248\n",
      "00:18:27 06 | 11 |  0.0506 |  0.0407\n",
      "00:18:37 06 | 12 |  0.0593 |  0.0582\n",
      "00:18:46 06 | 13 |  0.0418 |  0.0372\n",
      "00:18:56 06 | 14 |  0.0487 |  0.0660\n",
      "00:19:06 06 | 15 |  0.0113 | -0.0096\n",
      "00:19:16 06 | 16 |  0.0489 |  0.0496\n",
      "00:19:26 06 | 17 |  0.0238 |  0.0211\n",
      "00:19:36 06 | 18 |  0.0512 |  0.0535\n",
      "00:19:46 06 | 19 |  0.0223 | -0.0054\n",
      "00:19:56 06 | 20 |  0.0390 |  0.0479\n",
      "00:20:06 07 | 01 |  0.0139 |  0.0263\n",
      "00:20:16 07 | 02 |  0.0195 |  0.0211\n",
      "00:20:26 07 | 03 |  0.0026 | -0.0298\n",
      "00:20:36 07 | 04 | -0.0222 | -0.0121\n",
      "00:20:46 07 | 05 |  0.0233 | -0.0051\n",
      "00:20:56 07 | 06 |  0.0215 |  0.0504\n",
      "00:21:05 07 | 07 |  0.0178 |  0.0271\n",
      "00:21:15 07 | 08 |  0.0096 |  0.0223\n",
      "00:21:25 07 | 09 |  0.0401 |  0.0385\n",
      "00:21:35 07 | 10 |  0.0125 |  0.0298\n",
      "00:21:45 07 | 11 |  0.0253 |  0.0417\n",
      "00:21:55 07 | 12 |  0.0077 | -0.0056\n",
      "00:22:05 07 | 13 |  0.0371 |  0.0301\n",
      "00:22:15 07 | 14 |  0.0322 |  0.0329\n",
      "00:22:24 07 | 15 | -0.0249 | -0.0124\n",
      "00:22:34 07 | 16 |  0.0425 |  0.0348\n",
      "00:22:44 07 | 17 |  0.0338 |  0.0423\n",
      "00:22:54 07 | 18 |  0.0169 |  0.0376\n",
      "00:23:04 07 | 19 | -0.0100 |  0.0165\n",
      "00:23:14 07 | 20 |  0.0026 | -0.0022\n",
      "00:23:25 08 | 01 | -0.0049 | -0.0138\n",
      "00:23:35 08 | 02 | -0.0125 | -0.0180\n",
      "00:23:44 08 | 03 |  0.0085 |  0.0092\n",
      "00:23:54 08 | 04 |  0.0027 | -0.0048\n",
      "00:24:04 08 | 05 |  0.0262 |  0.0167\n",
      "00:24:14 08 | 06 |  0.0113 | -0.0197\n",
      "00:24:24 08 | 07 |  0.0005 | -0.0167\n",
      "00:24:33 08 | 08 |  0.0097 |  0.0077\n",
      "00:24:43 08 | 09 |  0.0058 | -0.0090\n",
      "00:24:53 08 | 10 |  0.0283 |  0.0321\n",
      "00:25:03 08 | 11 | -0.0002 | -0.0084\n",
      "00:25:13 08 | 12 |  0.0125 |  0.0157\n",
      "00:25:22 08 | 13 |  0.0066 |  0.0214\n",
      "00:25:32 08 | 14 | -0.0108 | -0.0218\n",
      "00:25:42 08 | 15 | -0.0009 | -0.0102\n",
      "00:25:52 08 | 16 |  0.0192 |  0.0302\n",
      "00:26:02 08 | 17 |  0.0082 |  0.0021\n",
      "00:26:11 08 | 18 |  0.0021 |  0.0045\n",
      "00:26:21 08 | 19 |  0.0076 |  0.0021\n",
      "00:26:31 08 | 20 |  0.0043 | -0.0024\n",
      "00:26:42 09 | 01 |  0.0283 |  0.0355\n",
      "00:26:52 09 | 02 | -0.0050 | -0.0150\n",
      "00:27:02 09 | 03 |  0.0134 |  0.0108\n",
      "00:27:12 09 | 04 |  0.0144 |  0.0222\n",
      "00:27:21 09 | 05 |  0.0242 |  0.0125\n",
      "00:27:31 09 | 06 |  0.0034 |  0.0119\n",
      "00:27:41 09 | 07 |  0.0167 |  0.0065\n",
      "00:27:51 09 | 08 |  0.0214 |  0.0099\n",
      "00:28:01 09 | 09 |  0.0169 | -0.0074\n",
      "00:28:11 09 | 10 |  0.0005 | -0.0106\n",
      "00:28:21 09 | 11 |  0.0184 |  0.0295\n",
      "00:28:31 09 | 12 |  0.0134 |  0.0202\n",
      "00:28:41 09 | 13 |  0.0189 |  0.0142\n",
      "00:28:51 09 | 14 |  0.0106 |  0.0118\n",
      "00:29:01 09 | 15 |  0.0215 |  0.0145\n",
      "00:29:11 09 | 16 |  0.0215 |  0.0269\n",
      "00:29:21 09 | 17 |  0.0178 |  0.0460\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:29:31 09 | 18 |  0.0104 |  0.0230\n",
      "00:29:41 09 | 19 |  0.0151 |  0.0196\n",
      "00:29:51 09 | 20 |  0.0135 |  0.0027\n",
      "00:30:01 10 | 01 |  0.0059 |  0.0135\n",
      "00:30:11 10 | 02 | -0.0130 | -0.0214\n",
      "00:30:21 10 | 03 |  0.0116 |  0.0223\n",
      "00:30:31 10 | 04 |  0.0012 |  0.0075\n",
      "00:30:41 10 | 05 |  0.0124 |  0.0363\n",
      "00:30:51 10 | 06 | -0.0051 |  0.0149\n",
      "00:31:01 10 | 07 | -0.0176 | -0.0006\n",
      "00:31:11 10 | 08 | -0.0202 | -0.0165\n",
      "00:31:21 10 | 09 | -0.0162 | -0.0074\n",
      "00:31:31 10 | 10 | -0.0391 | -0.0391\n",
      "00:31:41 10 | 11 | -0.0214 | -0.0146\n",
      "00:31:51 10 | 12 | -0.0407 | -0.0273\n",
      "00:32:01 10 | 13 | -0.0228 | -0.0240\n",
      "00:32:12 10 | 14 | -0.0453 | -0.0320\n",
      "00:32:22 10 | 15 | -0.0073 |  0.0009\n",
      "00:32:32 10 | 16 | -0.0426 | -0.0434\n",
      "00:32:42 10 | 17 | -0.0070 |  0.0039\n",
      "00:32:52 10 | 18 | -0.0065 |  0.0008\n",
      "00:33:02 10 | 19 | -0.0206 |  0.0063\n",
      "00:33:12 10 | 20 | -0.0132 |  0.0080\n",
      "00:33:22 11 | 01 |  0.0401 |  0.0424\n",
      "00:33:32 11 | 02 |  0.0075 |  0.0153\n",
      "00:33:42 11 | 03 | -0.0043 | -0.0046\n",
      "00:33:52 11 | 04 |  0.0185 |  0.0227\n",
      "00:34:02 11 | 05 |  0.0056 |  0.0085\n",
      "00:34:12 11 | 06 |  0.0324 |  0.0258\n",
      "00:34:22 11 | 07 |  0.0241 |  0.0293\n",
      "00:34:32 11 | 08 |  0.0174 |  0.0250\n",
      "00:34:42 11 | 09 |  0.0248 |  0.0112\n",
      "00:34:52 11 | 10 |  0.0226 |  0.0068\n",
      "00:35:02 11 | 11 |  0.0326 |  0.0207\n",
      "00:35:12 11 | 12 |  0.0257 |  0.0178\n",
      "00:35:22 11 | 13 |  0.0348 |  0.0319\n",
      "00:35:32 11 | 14 |  0.0323 |  0.0301\n",
      "00:35:42 11 | 15 | -0.0008 |  0.0076\n",
      "00:35:52 11 | 16 |  0.0074 |  0.0072\n",
      "00:36:02 11 | 17 |  0.0217 |  0.0217\n",
      "00:36:12 11 | 18 |  0.0022 |  0.0063\n",
      "00:36:22 11 | 19 | -0.0178 | -0.0254\n",
      "00:36:32 11 | 20 |  0.0346 |  0.0367\n",
      "00:36:42 12 | 01 |  0.0228 |  0.0185\n",
      "00:36:52 12 | 02 |  0.0064 |  0.0128\n",
      "00:37:02 12 | 03 |  0.0089 |  0.0177\n",
      "00:37:12 12 | 04 |  0.0039 | -0.0107\n",
      "00:37:22 12 | 05 | -0.0057 | -0.0095\n",
      "00:37:32 12 | 06 |  0.0047 | -0.0105\n",
      "00:37:42 12 | 07 | -0.0063 | -0.0069\n",
      "00:37:51 12 | 08 |  0.0002 | -0.0183\n",
      "00:38:01 12 | 09 |  0.0057 |  0.0173\n",
      "00:38:11 12 | 10 |  0.0013 | -0.0036\n",
      "00:38:21 12 | 11 | -0.0042 | -0.0158\n",
      "00:38:31 12 | 12 | -0.0028 | -0.0229\n",
      "00:38:41 12 | 13 |  0.0015 |  0.0004\n",
      "00:38:51 12 | 14 | -0.0102 | -0.0081\n",
      "00:39:01 12 | 15 | -0.0044 |  0.0131\n",
      "00:39:11 12 | 16 | -0.0084 | -0.0163\n",
      "00:39:21 12 | 17 | -0.0128 | -0.0290\n",
      "00:39:30 12 | 18 | -0.0131 | -0.0210\n",
      "00:39:40 12 | 19 | -0.0129 | -0.0074\n",
      "00:39:50 12 | 20 |  0.0058 | -0.0035\n",
      "(64, 32) tanh 0.2 256\n",
      "00:00:04 01 | 01 |  0.0108 |  0.0149\n",
      "00:00:07 01 | 02 |  0.0054 |  0.0110\n",
      "00:00:10 01 | 03 |  0.0054 |  0.0043\n",
      "00:00:13 01 | 04 |  0.0059 |  0.0032\n",
      "00:00:16 01 | 05 |  0.0103 |  0.0001\n",
      "00:00:19 01 | 06 |  0.0200 |  0.0145\n",
      "00:00:22 01 | 07 |  0.0120 |  0.0087\n",
      "00:00:25 01 | 08 | -0.0054 | -0.0051\n",
      "00:00:28 01 | 09 |  0.0043 |  0.0021\n",
      "00:00:31 01 | 10 |  0.0068 |  0.0072\n",
      "00:00:34 01 | 11 |  0.0045 | -0.0078\n",
      "00:00:37 01 | 12 |  0.0006 |  0.0036\n",
      "00:00:40 01 | 13 |  0.0165 |  0.0192\n",
      "00:00:42 01 | 14 |  0.0070 |  0.0119\n",
      "00:00:45 01 | 15 |  0.0199 |  0.0171\n",
      "00:00:48 01 | 16 |  0.0098 |  0.0100\n",
      "00:00:52 01 | 17 |  0.0204 |  0.0093\n",
      "00:00:55 01 | 18 |  0.0107 |  0.0116\n",
      "00:00:57 01 | 19 |  0.0075 |  0.0064\n",
      "00:01:00 01 | 20 |  0.0020 |  0.0092\n",
      "00:01:04 02 | 01 | -0.0097 | -0.0128\n",
      "00:01:07 02 | 02 | -0.0315 | -0.0098\n",
      "00:01:10 02 | 03 |  0.0022 |  0.0078\n",
      "00:01:13 02 | 04 |  0.0134 |  0.0174\n",
      "00:01:16 02 | 05 |  0.0210 |  0.0278\n",
      "00:01:19 02 | 06 |  0.0211 |  0.0293\n",
      "00:01:22 02 | 07 | -0.0298 | -0.0188\n",
      "00:01:25 02 | 08 | -0.0153 | -0.0160\n",
      "00:01:28 02 | 09 | -0.0073 | -0.0013\n",
      "00:01:31 02 | 10 | -0.0066 | -0.0149\n",
      "00:01:34 02 | 11 |  0.0114 |  0.0289\n",
      "00:01:37 02 | 12 |  0.0128 |  0.0268\n",
      "00:01:40 02 | 13 |  0.0162 |  0.0186\n",
      "00:01:43 02 | 14 |  0.0244 |  0.0215\n",
      "00:01:46 02 | 15 |  0.0089 |  0.0037\n",
      "00:01:49 02 | 16 |  0.0094 |  0.0075\n",
      "00:01:52 02 | 17 |  0.0267 |  0.0367\n",
      "00:01:55 02 | 18 |  0.0142 |  0.0052\n",
      "00:01:58 02 | 19 |  0.0091 |  0.0292\n",
      "00:02:01 02 | 20 |  0.0150 |  0.0290\n",
      "00:02:04 03 | 01 | -0.0212 | -0.0305\n",
      "00:02:07 03 | 02 | -0.0208 | -0.0381\n",
      "00:02:10 03 | 03 |  0.0257 |  0.0512\n",
      "00:02:13 03 | 04 |  0.0129 |  0.0197\n",
      "00:02:16 03 | 05 |  0.0060 | -0.0203\n",
      "00:02:19 03 | 06 | -0.0029 | -0.0150\n",
      "00:02:22 03 | 07 |  0.0314 |  0.0322\n",
      "00:02:25 03 | 08 |  0.0046 |  0.0066\n",
      "00:02:28 03 | 09 | -0.0238 | -0.0351\n",
      "00:02:31 03 | 10 |  0.0277 |  0.0322\n",
      "00:02:34 03 | 11 |  0.0057 |  0.0035\n",
      "00:02:37 03 | 12 | -0.0027 |  0.0129\n",
      "00:02:40 03 | 13 |  0.0076 |  0.0172\n",
      "00:02:43 03 | 14 |  0.0061 | -0.0024\n",
      "00:02:46 03 | 15 | -0.0108 | -0.0321\n",
      "00:02:49 03 | 16 | -0.0062 | -0.0135\n",
      "00:02:52 03 | 17 | -0.0010 |  0.0081\n",
      "00:02:55 03 | 18 | -0.0108 | -0.0166\n",
      "00:02:58 03 | 19 |  0.0005 |  0.0009\n",
      "00:03:01 03 | 20 |  0.0007 | -0.0228\n",
      "00:03:05 04 | 01 | -0.0204 | -0.0180\n",
      "00:03:08 04 | 02 |  0.0059 | -0.0030\n",
      "00:03:11 04 | 03 |  0.0288 |  0.0076\n",
      "00:03:14 04 | 04 |  0.0066 | -0.0006\n",
      "00:03:17 04 | 05 |  0.0384 |  0.0369\n",
      "00:03:20 04 | 06 | -0.0190 | -0.0169\n",
      "00:03:23 04 | 07 | -0.0031 | -0.0051\n",
      "00:03:26 04 | 08 | -0.0107 | -0.0177\n",
      "00:03:29 04 | 09 | -0.0093 | -0.0294\n",
      "00:03:32 04 | 10 | -0.0126 | -0.0050\n",
      "00:03:35 04 | 11 | -0.0143 | -0.0056\n",
      "00:03:38 04 | 12 |  0.0059 |  0.0140\n",
      "00:03:41 04 | 13 | -0.0110 | -0.0070\n",
      "00:03:44 04 | 14 | -0.0045 |  0.0086\n",
      "00:03:47 04 | 15 | -0.0261 | -0.0406\n",
      "00:03:50 04 | 16 | -0.0281 | -0.0329\n",
      "00:03:53 04 | 17 | -0.0138 | -0.0222\n",
      "00:03:56 04 | 18 | -0.0071 | -0.0172\n",
      "00:03:59 04 | 19 | -0.0145 | -0.0035\n",
      "00:04:02 04 | 20 | -0.0238 | -0.0279\n",
      "00:04:06 05 | 01 |  0.0104 |  0.0066\n",
      "00:04:09 05 | 02 | -0.0033 | -0.0071\n",
      "00:04:12 05 | 03 |  0.0396 |  0.0601\n",
      "00:04:15 05 | 04 |  0.0077 |  0.0068\n",
      "00:04:18 05 | 05 | -0.0067 |  0.0056\n",
      "00:04:21 05 | 06 |  0.0053 | -0.0000\n",
      "00:04:24 05 | 07 |  0.0208 |  0.0185\n",
      "00:04:27 05 | 08 |  0.0128 |  0.0173\n",
      "00:04:30 05 | 09 |  0.0206 |  0.0089\n",
      "00:04:33 05 | 10 |  0.0062 | -0.0035\n",
      "00:04:36 05 | 11 |  0.0002 | -0.0160\n",
      "00:04:39 05 | 12 | -0.0064 | -0.0039\n",
      "00:04:42 05 | 13 |  0.0051 |  0.0014\n",
      "00:04:45 05 | 14 | -0.0073 |  0.0025\n",
      "00:04:48 05 | 15 |  0.0052 |  0.0020\n",
      "00:04:51 05 | 16 |  0.0003 |  0.0078\n",
      "00:04:54 05 | 17 | -0.0057 |  0.0067\n",
      "00:04:57 05 | 18 | -0.0084 |  0.0005\n",
      "00:04:60 05 | 19 | -0.0064 |  0.0080\n",
      "00:05:03 05 | 20 | -0.0015 | -0.0007\n",
      "00:05:07 06 | 01 |  0.0101 |  0.0090\n",
      "00:05:10 06 | 02 |  0.0184 |  0.0393\n",
      "00:05:13 06 | 03 |  0.0367 |  0.0162\n",
      "00:05:16 06 | 04 |  0.0398 |  0.0088\n",
      "00:05:18 06 | 05 |  0.0032 |  0.0173\n",
      "00:05:21 06 | 06 |  0.0277 |  0.0396\n",
      "00:05:24 06 | 07 |  0.0338 |  0.0264\n",
      "00:05:27 06 | 08 |  0.0125 |  0.0041\n",
      "00:05:30 06 | 09 |  0.0190 |  0.0051\n",
      "00:05:33 06 | 10 |  0.0271 |  0.0011\n",
      "00:05:36 06 | 11 |  0.0248 |  0.0205\n",
      "00:05:39 06 | 12 |  0.0239 |  0.0248\n",
      "00:05:42 06 | 13 |  0.0137 |  0.0095\n",
      "00:05:45 06 | 14 |  0.0169 | -0.0003\n",
      "00:05:48 06 | 15 |  0.0292 |  0.0202\n",
      "00:05:51 06 | 16 |  0.0105 |  0.0198\n",
      "00:05:54 06 | 17 |  0.0216 |  0.0117\n",
      "00:05:57 06 | 18 |  0.0378 |  0.0108\n",
      "00:06:00 06 | 19 |  0.0184 |  0.0065\n",
      "00:06:03 06 | 20 |  0.0124 | -0.0034\n",
      "00:06:07 07 | 01 |  0.0112 |  0.0111\n",
      "00:06:10 07 | 02 |  0.0110 |  0.0339\n",
      "00:06:13 07 | 03 |  0.0039 |  0.0281\n",
      "00:06:16 07 | 04 |  0.0034 |  0.0086\n",
      "00:06:19 07 | 05 |  0.0315 |  0.0319\n",
      "00:06:22 07 | 06 |  0.0294 |  0.0295\n",
      "00:06:25 07 | 07 |  0.0232 |  0.0321\n",
      "00:06:28 07 | 08 |  0.0530 |  0.0429\n",
      "00:06:31 07 | 09 |  0.0110 |  0.0074\n",
      "00:06:34 07 | 10 |  0.0263 |  0.0325\n",
      "00:06:37 07 | 11 |  0.0312 |  0.0491\n",
      "00:06:40 07 | 12 |  0.0113 |  0.0299\n",
      "00:06:43 07 | 13 |  0.0109 |  0.0230\n",
      "00:06:46 07 | 14 |  0.0306 |  0.0296\n",
      "00:06:49 07 | 15 |  0.0074 |  0.0122\n",
      "00:06:52 07 | 16 |  0.0433 |  0.0306\n",
      "00:06:55 07 | 17 |  0.0157 |  0.0266\n",
      "00:06:57 07 | 18 |  0.0143 |  0.0162\n",
      "00:07:00 07 | 19 |  0.0238 |  0.0172\n",
      "00:07:03 07 | 20 |  0.0216 |  0.0219\n",
      "00:07:07 08 | 01 | -0.0057 | -0.0003\n",
      "00:07:10 08 | 02 | -0.0036 |  0.0139\n",
      "00:07:13 08 | 03 |  0.0137 |  0.0119\n",
      "00:07:16 08 | 04 |  0.0268 |  0.0183\n",
      "00:07:19 08 | 05 | -0.0034 |  0.0016\n",
      "00:07:22 08 | 06 |  0.0212 |  0.0204\n",
      "00:07:25 08 | 07 |  0.0114 |  0.0252\n",
      "00:07:28 08 | 08 |  0.0254 |  0.0204\n",
      "00:07:31 08 | 09 |  0.0183 |  0.0064\n",
      "00:07:34 08 | 10 |  0.0257 |  0.0211\n",
      "00:07:37 08 | 11 |  0.0260 |  0.0233\n",
      "00:07:39 08 | 12 |  0.0237 |  0.0268\n",
      "00:07:42 08 | 13 |  0.0143 |  0.0198\n",
      "00:07:45 08 | 14 |  0.0106 |  0.0075\n",
      "00:07:48 08 | 15 |  0.0100 |  0.0187\n",
      "00:07:51 08 | 16 |  0.0101 |  0.0242\n",
      "00:07:54 08 | 17 |  0.0107 |  0.0175\n",
      "00:07:57 08 | 18 |  0.0101 |  0.0115\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:07:60 08 | 19 |  0.0035 |  0.0003\n",
      "00:08:03 08 | 20 |  0.0002 |  0.0048\n",
      "00:08:07 09 | 01 |  0.0019 |  0.0022\n",
      "00:08:10 09 | 02 |  0.0077 |  0.0087\n",
      "00:08:13 09 | 03 |  0.0211 |  0.0087\n",
      "00:08:16 09 | 04 |  0.0158 |  0.0248\n",
      "00:08:19 09 | 05 |  0.0024 | -0.0075\n",
      "00:08:21 09 | 06 | -0.0062 |  0.0075\n",
      "00:08:24 09 | 07 |  0.0041 |  0.0104\n",
      "00:08:27 09 | 08 |  0.0016 |  0.0024\n",
      "00:08:30 09 | 09 |  0.0196 |  0.0194\n",
      "00:08:33 09 | 10 |  0.0245 |  0.0129\n",
      "00:08:36 09 | 11 |  0.0214 |  0.0394\n",
      "00:08:39 09 | 12 |  0.0061 |  0.0159\n",
      "00:08:42 09 | 13 |  0.0107 |  0.0094\n",
      "00:08:45 09 | 14 |  0.0114 | -0.0011\n",
      "00:08:48 09 | 15 |  0.0120 |  0.0166\n",
      "00:08:51 09 | 16 |  0.0161 |  0.0070\n",
      "00:08:54 09 | 17 |  0.0136 |  0.0515\n",
      "00:08:57 09 | 18 |  0.0063 |  0.0273\n",
      "00:09:00 09 | 19 |  0.0097 |  0.0240\n",
      "00:09:03 09 | 20 |  0.0196 |  0.0297\n",
      "00:09:07 10 | 01 | -0.0189 | -0.0013\n",
      "00:09:10 10 | 02 |  0.0255 |  0.0286\n",
      "00:09:13 10 | 03 |  0.0077 |  0.0044\n",
      "00:09:16 10 | 04 | -0.0396 | -0.0257\n",
      "00:09:19 10 | 05 | -0.0420 | -0.0506\n",
      "00:09:22 10 | 06 | -0.0234 | -0.0223\n",
      "00:09:25 10 | 07 |  0.0205 |  0.0241\n",
      "00:09:28 10 | 08 |  0.0446 |  0.0422\n",
      "00:09:31 10 | 09 |  0.0223 |  0.0222\n",
      "00:09:34 10 | 10 |  0.0417 |  0.0436\n",
      "00:09:36 10 | 11 | -0.0132 | -0.0230\n",
      "00:09:39 10 | 12 | -0.0370 | -0.0435\n",
      "00:09:42 10 | 13 | -0.0045 | -0.0187\n",
      "00:09:45 10 | 14 | -0.0022 | -0.0141\n",
      "00:09:48 10 | 15 | -0.0076 |  0.0003\n",
      "00:09:51 10 | 16 | -0.0185 | -0.0071\n",
      "00:09:54 10 | 17 | -0.0275 | -0.0308\n",
      "00:09:57 10 | 18 | -0.0381 | -0.0397\n",
      "00:10:00 10 | 19 | -0.0451 | -0.0546\n",
      "00:10:03 10 | 20 | -0.0234 | -0.0223\n",
      "00:10:07 11 | 01 |  0.0306 |  0.0189\n",
      "00:10:10 11 | 02 |  0.0034 |  0.0014\n",
      "00:10:13 11 | 03 |  0.0214 |  0.0179\n",
      "00:10:16 11 | 04 |  0.0125 |  0.0208\n",
      "00:10:19 11 | 05 |  0.0005 |  0.0088\n",
      "00:10:22 11 | 06 |  0.0304 |  0.0198\n",
      "00:10:25 11 | 07 |  0.0259 |  0.0203\n",
      "00:10:28 11 | 08 |  0.0131 |  0.0084\n",
      "00:10:31 11 | 09 |  0.0181 |  0.0119\n",
      "00:10:34 11 | 10 |  0.0020 | -0.0141\n",
      "00:10:37 11 | 11 |  0.0286 |  0.0435\n",
      "00:10:39 11 | 12 |  0.0285 |  0.0250\n",
      "00:10:42 11 | 13 |  0.0254 |  0.0195\n",
      "00:10:45 11 | 14 |  0.0114 |  0.0089\n",
      "00:10:48 11 | 15 |  0.0285 |  0.0272\n",
      "00:10:51 11 | 16 |  0.0218 |  0.0072\n",
      "00:10:54 11 | 17 |  0.0232 |  0.0079\n",
      "00:10:57 11 | 18 |  0.0115 |  0.0086\n",
      "00:11:00 11 | 19 |  0.0040 |  0.0091\n",
      "00:11:03 11 | 20 |  0.0269 |  0.0204\n",
      "00:11:07 12 | 01 |  0.0098 |  0.0029\n",
      "00:11:10 12 | 02 |  0.0073 |  0.0038\n",
      "00:11:13 12 | 03 |  0.0348 |  0.0457\n",
      "00:11:16 12 | 04 |  0.0142 |  0.0020\n",
      "00:11:19 12 | 05 |  0.0077 |  0.0050\n",
      "00:11:22 12 | 06 |  0.0053 | -0.0012\n",
      "00:11:25 12 | 07 | -0.0066 | -0.0125\n",
      "00:11:28 12 | 08 |  0.0111 |  0.0054\n",
      "00:11:30 12 | 09 |  0.0242 |  0.0310\n",
      "00:11:33 12 | 10 |  0.0045 |  0.0012\n",
      "00:11:36 12 | 11 |  0.0061 |  0.0018\n",
      "00:11:39 12 | 12 |  0.0007 |  0.0242\n",
      "00:11:42 12 | 13 | -0.0044 | -0.0033\n",
      "00:11:45 12 | 14 |  0.0012 | -0.0102\n",
      "00:11:48 12 | 15 |  0.0051 | -0.0002\n",
      "00:11:51 12 | 16 |  0.0042 | -0.0211\n",
      "00:11:54 12 | 17 |  0.0068 |  0.0126\n",
      "00:11:57 12 | 18 | -0.0009 |  0.0056\n",
      "00:12:00 12 | 19 | -0.0041 | -0.0211\n",
      "00:12:03 12 | 20 |  0.0031 |  0.0040\n",
      "(32, 16) tanh 0.1 64\n",
      "00:00:10 01 | 01 |  0.0117 |  0.0270\n",
      "00:00:20 01 | 02 |  0.0106 |  0.0179\n",
      "00:00:30 01 | 03 |  0.0118 |  0.0075\n",
      "00:00:40 01 | 04 |  0.0079 |  0.0030\n",
      "00:00:50 01 | 05 |  0.0128 |  0.0150\n",
      "00:00:59 01 | 06 |  0.0069 |  0.0242\n",
      "00:01:09 01 | 07 | -0.0020 |  0.0015\n",
      "00:01:19 01 | 08 |  0.0105 |  0.0058\n",
      "00:01:29 01 | 09 |  0.0082 |  0.0018\n",
      "00:01:38 01 | 10 |  0.0134 |  0.0135\n",
      "00:01:48 01 | 11 |  0.0150 |  0.0243\n",
      "00:01:58 01 | 12 |  0.0048 | -0.0064\n",
      "00:02:08 01 | 13 |  0.0227 |  0.0284\n",
      "00:02:17 01 | 14 |  0.0144 |  0.0281\n",
      "00:02:27 01 | 15 |  0.0144 |  0.0105\n",
      "00:02:37 01 | 16 |  0.0091 |  0.0229\n",
      "00:02:47 01 | 17 |  0.0113 |  0.0148\n",
      "00:02:56 01 | 18 |  0.0022 | -0.0042\n",
      "00:03:06 01 | 19 | -0.0036 | -0.0159\n",
      "00:03:16 01 | 20 | -0.0036 | -0.0103\n",
      "00:03:27 02 | 01 |  0.0205 |  0.0506\n",
      "00:03:36 02 | 02 |  0.0250 |  0.0144\n",
      "00:03:46 02 | 03 | -0.0006 |  0.0053\n",
      "00:03:56 02 | 04 |  0.0045 | -0.0036\n",
      "00:04:06 02 | 05 | -0.0163 |  0.0053\n",
      "00:04:15 02 | 06 | -0.0057 | -0.0149\n",
      "00:04:25 02 | 07 | -0.0020 | -0.0044\n",
      "00:04:35 02 | 08 |  0.0142 |  0.0137\n",
      "00:04:44 02 | 09 | -0.0028 |  0.0077\n",
      "00:04:55 02 | 10 |  0.0060 |  0.0149\n",
      "00:05:04 02 | 11 | -0.0053 |  0.0068\n",
      "00:05:14 02 | 12 |  0.0019 |  0.0134\n",
      "00:05:24 02 | 13 |  0.0045 |  0.0120\n",
      "00:05:34 02 | 14 | -0.0048 |  0.0045\n",
      "00:05:43 02 | 15 | -0.0039 | -0.0027\n",
      "00:05:53 02 | 16 |  0.0073 |  0.0182\n",
      "00:06:03 02 | 17 |  0.0097 |  0.0100\n",
      "00:06:13 02 | 18 | -0.0021 |  0.0067\n",
      "00:06:22 02 | 19 | -0.0140 | -0.0070\n",
      "00:06:32 02 | 20 | -0.0058 |  0.0015\n",
      "00:06:42 03 | 01 |  0.0089 |  0.0170\n",
      "00:06:52 03 | 02 | -0.0175 | -0.0168\n",
      "00:07:02 03 | 03 |  0.0007 | -0.0237\n",
      "00:07:12 03 | 04 | -0.0004 |  0.0104\n",
      "00:07:22 03 | 05 |  0.0010 |  0.0054\n",
      "00:07:32 03 | 06 |  0.0092 |  0.0217\n",
      "00:07:42 03 | 07 | -0.0022 | -0.0191\n",
      "00:07:52 03 | 08 |  0.0163 |  0.0241\n",
      "00:08:01 03 | 09 | -0.0071 | -0.0246\n",
      "00:08:11 03 | 10 | -0.0092 | -0.0197\n",
      "00:08:21 03 | 11 |  0.0160 |  0.0254\n",
      "00:08:31 03 | 12 |  0.0228 |  0.0334\n",
      "00:08:41 03 | 13 |  0.0023 |  0.0215\n",
      "00:08:51 03 | 14 |  0.0055 |  0.0041\n",
      "00:09:01 03 | 15 | -0.0020 |  0.0017\n",
      "00:09:11 03 | 16 | -0.0013 | -0.0360\n",
      "00:09:21 03 | 17 | -0.0125 |  0.0043\n",
      "00:09:31 03 | 18 | -0.0282 | -0.0462\n",
      "00:09:41 03 | 19 | -0.0104 | -0.0255\n",
      "00:09:51 03 | 20 |  0.0064 |  0.0048\n",
      "00:10:01 04 | 01 | -0.0192 | -0.0146\n",
      "00:10:11 04 | 02 | -0.0471 | -0.0533\n",
      "00:10:21 04 | 03 |  0.0202 |  0.0527\n",
      "00:10:31 04 | 04 |  0.0192 | -0.0035\n",
      "00:10:41 04 | 05 |  0.0256 |  0.0280\n",
      "00:10:51 04 | 06 | -0.0212 | -0.0097\n",
      "00:11:01 04 | 07 | -0.0015 |  0.0168\n",
      "00:11:11 04 | 08 | -0.0195 | -0.0101\n",
      "00:11:21 04 | 09 | -0.0041 | -0.0151\n",
      "00:11:31 04 | 10 |  0.0008 |  0.0110\n",
      "00:11:41 04 | 11 |  0.0140 |  0.0272\n",
      "00:11:51 04 | 12 | -0.0023 | -0.0048\n",
      "00:12:01 04 | 13 |  0.0181 |  0.0250\n",
      "00:12:11 04 | 14 | -0.0021 |  0.0080\n",
      "00:12:21 04 | 15 | -0.0089 |  0.0106\n",
      "00:12:31 04 | 16 | -0.0116 | -0.0099\n",
      "00:12:41 04 | 17 |  0.0104 |  0.0035\n",
      "00:12:51 04 | 18 | -0.0074 |  0.0054\n",
      "00:13:00 04 | 19 | -0.0100 | -0.0106\n",
      "00:13:10 04 | 20 |  0.0183 |  0.0208\n",
      "00:13:21 05 | 01 |  0.0046 | -0.0068\n",
      "00:13:31 05 | 02 |  0.0087 |  0.0133\n",
      "00:13:41 05 | 03 |  0.0116 |  0.0103\n",
      "00:13:51 05 | 04 |  0.0044 |  0.0004\n",
      "00:14:00 05 | 05 | -0.0070 | -0.0051\n",
      "00:14:10 05 | 06 |  0.0050 |  0.0080\n",
      "00:14:20 05 | 07 |  0.0149 |  0.0150\n",
      "00:14:30 05 | 08 | -0.0088 | -0.0102\n",
      "00:14:40 05 | 09 |  0.0044 |  0.0127\n",
      "00:14:50 05 | 10 |  0.0030 | -0.0012\n",
      "00:14:59 05 | 11 | -0.0029 | -0.0059\n",
      "00:15:09 05 | 12 |  0.0006 |  0.0010\n",
      "00:15:19 05 | 13 |  0.0128 |  0.0096\n",
      "00:15:29 05 | 14 |  0.0026 |  0.0021\n",
      "00:15:39 05 | 15 | -0.0060 | -0.0048\n",
      "00:15:49 05 | 16 |  0.0001 | -0.0155\n",
      "00:15:58 05 | 17 | -0.0057 | -0.0149\n",
      "00:16:08 05 | 18 | -0.0203 | -0.0185\n",
      "00:16:18 05 | 19 | -0.0150 | -0.0048\n",
      "00:16:28 05 | 20 | -0.0181 | -0.0321\n",
      "00:16:39 06 | 01 |  0.0127 | -0.0054\n",
      "00:16:48 06 | 02 |  0.0203 |  0.0148\n",
      "00:16:58 06 | 03 |  0.0171 |  0.0144\n",
      "00:17:08 06 | 04 |  0.0315 |  0.0168\n",
      "00:17:18 06 | 05 |  0.0078 | -0.0010\n",
      "00:17:28 06 | 06 |  0.0263 |  0.0144\n",
      "00:17:38 06 | 07 |  0.0139 |  0.0185\n",
      "00:17:48 06 | 08 |  0.0177 |  0.0191\n",
      "00:17:57 06 | 09 |  0.0144 |  0.0191\n",
      "00:18:07 06 | 10 |  0.0263 |  0.0151\n",
      "00:18:17 06 | 11 |  0.0259 |  0.0091\n",
      "00:18:27 06 | 12 |  0.0258 |  0.0102\n",
      "00:18:37 06 | 13 |  0.0212 | -0.0072\n",
      "00:18:47 06 | 14 |  0.0300 |  0.0449\n",
      "00:18:57 06 | 15 |  0.0334 |  0.0255\n",
      "00:19:06 06 | 16 |  0.0328 |  0.0173\n",
      "00:19:16 06 | 17 |  0.0274 |  0.0422\n",
      "00:19:26 06 | 18 |  0.0154 |  0.0036\n",
      "00:19:36 06 | 19 |  0.0348 |  0.0289\n",
      "00:19:46 06 | 20 |  0.0098 | -0.0051\n",
      "00:19:56 07 | 01 | -0.0052 | -0.0194\n",
      "00:20:06 07 | 02 | -0.0026 |  0.0068\n",
      "00:20:16 07 | 03 |  0.0087 |  0.0419\n",
      "00:20:26 07 | 04 |  0.0205 |  0.0262\n",
      "00:20:36 07 | 05 |  0.0112 |  0.0091\n",
      "00:20:46 07 | 06 |  0.0052 |  0.0352\n",
      "00:20:55 07 | 07 |  0.0035 | -0.0112\n",
      "00:21:05 07 | 08 |  0.0121 |  0.0028\n",
      "00:21:15 07 | 09 |  0.0025 |  0.0144\n",
      "00:21:25 07 | 10 |  0.0147 |  0.0342\n",
      "00:21:35 07 | 11 |  0.0035 |  0.0136\n",
      "00:21:44 07 | 12 |  0.0143 |  0.0241\n",
      "00:21:54 07 | 13 |  0.0171 |  0.0127\n",
      "00:22:04 07 | 14 |  0.0188 |  0.0366\n",
      "00:22:14 07 | 15 |  0.0023 |  0.0021\n",
      "00:22:24 07 | 16 |  0.0140 |  0.0187\n",
      "00:22:33 07 | 17 |  0.0081 |  0.0060\n",
      "00:22:43 07 | 18 | -0.0009 |  0.0031\n",
      "00:22:53 07 | 19 |  0.0083 |  0.0137\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:23:03 07 | 20 |  0.0040 |  0.0225\n",
      "00:23:13 08 | 01 |  0.0012 |  0.0095\n",
      "00:23:23 08 | 02 | -0.0180 | -0.0106\n",
      "00:23:33 08 | 03 |  0.0236 |  0.0577\n",
      "00:23:43 08 | 04 |  0.0228 |  0.0471\n",
      "00:23:52 08 | 05 | -0.0006 | -0.0075\n",
      "00:24:02 08 | 06 |  0.0137 |  0.0199\n",
      "00:24:12 08 | 07 |  0.0158 |  0.0182\n",
      "00:24:22 08 | 08 |  0.0211 |  0.0310\n",
      "00:24:32 08 | 09 |  0.0224 |  0.0205\n",
      "00:24:41 08 | 10 |  0.0267 |  0.0198\n",
      "00:24:51 08 | 11 |  0.0287 |  0.0340\n",
      "00:25:01 08 | 12 |  0.0274 |  0.0315\n",
      "00:25:11 08 | 13 |  0.0359 |  0.0363\n",
      "00:25:21 08 | 14 |  0.0283 |  0.0273\n",
      "00:25:30 08 | 15 |  0.0253 |  0.0287\n",
      "00:25:40 08 | 16 |  0.0268 |  0.0270\n",
      "00:25:50 08 | 17 |  0.0115 | -0.0019\n",
      "00:25:60 08 | 18 |  0.0326 |  0.0327\n",
      "00:26:10 08 | 19 |  0.0164 |  0.0330\n",
      "00:26:19 08 | 20 |  0.0170 |  0.0286\n",
      "00:26:30 09 | 01 |  0.0094 |  0.0071\n",
      "00:26:40 09 | 02 | -0.0085 | -0.0101\n",
      "00:26:50 09 | 03 |  0.0047 |  0.0013\n",
      "00:26:59 09 | 04 |  0.0110 |  0.0080\n",
      "00:27:09 09 | 05 |  0.0010 |  0.0137\n",
      "00:27:19 09 | 06 | -0.0021 |  0.0014\n",
      "00:27:29 09 | 07 | -0.0014 |  0.0228\n",
      "00:27:38 09 | 08 | -0.0028 | -0.0074\n",
      "00:27:48 09 | 09 | -0.0007 |  0.0185\n",
      "00:27:58 09 | 10 |  0.0045 | -0.0005\n",
      "00:28:08 09 | 11 | -0.0023 | -0.0010\n",
      "00:28:17 09 | 12 | -0.0111 | -0.0054\n",
      "00:28:27 09 | 13 | -0.0035 | -0.0304\n",
      "00:28:37 09 | 14 |  0.0062 |  0.0148\n",
      "00:28:47 09 | 15 | -0.0082 | -0.0015\n",
      "00:28:57 09 | 16 |  0.0003 |  0.0056\n",
      "00:29:06 09 | 17 |  0.0011 | -0.0140\n",
      "00:29:16 09 | 18 | -0.0060 | -0.0124\n",
      "00:29:26 09 | 19 | -0.0050 | -0.0093\n",
      "00:29:36 09 | 20 |  0.0065 |  0.0082\n",
      "00:29:46 10 | 01 |  0.0294 |  0.0190\n",
      "00:29:56 10 | 02 |  0.0174 |  0.0252\n",
      "00:30:06 10 | 03 | -0.0253 | -0.0338\n",
      "00:30:16 10 | 04 |  0.0064 | -0.0143\n",
      "00:30:26 10 | 05 |  0.0445 |  0.0420\n",
      "00:30:35 10 | 06 | -0.0042 | -0.0227\n",
      "00:30:45 10 | 07 |  0.0097 |  0.0218\n",
      "00:30:55 10 | 08 |  0.0278 |  0.0405\n",
      "00:31:05 10 | 09 |  0.0251 |  0.0234\n",
      "00:31:15 10 | 10 |  0.0173 | -0.0019\n",
      "00:31:25 10 | 11 |  0.0068 | -0.0149\n",
      "00:31:34 10 | 12 |  0.0265 |  0.0228\n",
      "00:31:44 10 | 13 |  0.0051 | -0.0051\n",
      "00:31:54 10 | 14 |  0.0115 | -0.0015\n",
      "00:32:04 10 | 15 |  0.0242 |  0.0341\n",
      "00:32:14 10 | 16 |  0.0142 |  0.0170\n",
      "00:32:24 10 | 17 | -0.0018 |  0.0060\n",
      "00:32:34 10 | 18 | -0.0102 | -0.0073\n",
      "00:32:43 10 | 19 |  0.0037 |  0.0157\n",
      "00:32:53 10 | 20 | -0.0131 | -0.0125\n",
      "00:33:04 11 | 01 |  0.0187 |  0.0191\n",
      "00:33:13 11 | 02 | -0.0032 | -0.0024\n",
      "00:33:23 11 | 03 | -0.0037 | -0.0109\n",
      "00:33:33 11 | 04 |  0.0102 |  0.0045\n",
      "00:33:43 11 | 05 |  0.0299 |  0.0192\n",
      "00:33:53 11 | 06 |  0.0258 |  0.0281\n",
      "00:34:02 11 | 07 |  0.0081 |  0.0102\n",
      "00:34:12 11 | 08 | -0.0113 | -0.0187\n",
      "00:34:22 11 | 09 |  0.0127 |  0.0090\n",
      "00:34:32 11 | 10 | -0.0103 | -0.0142\n",
      "00:34:42 11 | 11 | -0.0102 | -0.0176\n",
      "00:34:51 11 | 12 |  0.0088 |  0.0228\n",
      "00:35:01 11 | 13 |  0.0066 |  0.0109\n",
      "00:35:11 11 | 14 |  0.0070 | -0.0010\n",
      "00:35:21 11 | 15 |  0.0180 |  0.0170\n",
      "00:35:31 11 | 16 | -0.0052 |  0.0033\n",
      "00:35:40 11 | 17 |  0.0247 |  0.0066\n",
      "00:35:50 11 | 18 | -0.0037 | -0.0057\n",
      "00:36:00 11 | 19 |  0.0228 |  0.0249\n",
      "00:36:10 11 | 20 |  0.0061 | -0.0039\n",
      "00:36:20 12 | 01 |  0.0114 |  0.0242\n",
      "00:36:30 12 | 02 |  0.0061 | -0.0074\n",
      "00:36:40 12 | 03 |  0.0056 |  0.0009\n",
      "00:36:50 12 | 04 | -0.0127 | -0.0225\n",
      "00:36:60 12 | 05 | -0.0027 | -0.0101\n",
      "00:37:09 12 | 06 | -0.0138 | -0.0085\n",
      "00:37:19 12 | 07 |  0.0002 | -0.0042\n",
      "00:37:29 12 | 08 |  0.0037 | -0.0019\n",
      "00:37:39 12 | 09 | -0.0005 | -0.0228\n",
      "00:37:49 12 | 10 |  0.0011 |  0.0041\n",
      "00:37:58 12 | 11 |  0.0060 | -0.0074\n",
      "00:38:08 12 | 12 |  0.0073 | -0.0004\n",
      "00:38:18 12 | 13 |  0.0088 |  0.0232\n",
      "00:38:28 12 | 14 |  0.0028 |  0.0245\n",
      "00:38:38 12 | 15 |  0.0035 | -0.0063\n",
      "00:38:48 12 | 16 | -0.0016 | -0.0025\n",
      "00:38:58 12 | 17 | -0.0099 | -0.0186\n",
      "00:39:07 12 | 18 | -0.0091 | -0.0115\n",
      "00:39:17 12 | 19 |  0.0012 |  0.0130\n",
      "00:39:27 12 | 20 |  0.0061 |  0.0054\n",
      "(32, 16) tanh 0.1 256\n",
      "00:00:04 01 | 01 |  0.0029 |  0.0090\n",
      "00:00:07 01 | 02 |  0.0126 |  0.0285\n",
      "00:00:10 01 | 03 |  0.0110 |  0.0291\n",
      "00:00:13 01 | 04 |  0.0003 |  0.0077\n",
      "00:00:16 01 | 05 |  0.0013 | -0.0129\n",
      "00:00:19 01 | 06 |  0.0035 |  0.0091\n",
      "00:00:22 01 | 07 |  0.0099 |  0.0044\n",
      "00:00:25 01 | 08 |  0.0069 | -0.0001\n",
      "00:00:28 01 | 09 | -0.0033 | -0.0101\n",
      "00:00:31 01 | 10 |  0.0123 |  0.0233\n",
      "00:00:34 01 | 11 |  0.0166 |  0.0138\n",
      "00:00:37 01 | 12 |  0.0196 |  0.0268\n",
      "00:00:40 01 | 13 |  0.0020 |  0.0016\n",
      "00:00:43 01 | 14 |  0.0097 |  0.0122\n",
      "00:00:46 01 | 15 | -0.0099 | -0.0020\n",
      "00:00:49 01 | 16 | -0.0002 |  0.0077\n",
      "00:00:52 01 | 17 |  0.0088 | -0.0067\n",
      "00:00:55 01 | 18 | -0.0036 | -0.0094\n",
      "00:00:58 01 | 19 | -0.0018 | -0.0107\n",
      "00:01:01 01 | 20 | -0.0076 | -0.0112\n",
      "00:01:04 02 | 01 |  0.0205 |  0.0152\n",
      "00:01:07 02 | 02 | -0.0404 | -0.0273\n",
      "00:01:10 02 | 03 | -0.0011 | -0.0094\n",
      "00:01:13 02 | 04 |  0.0161 |  0.0050\n",
      "00:01:16 02 | 05 |  0.0124 |  0.0156\n",
      "00:01:19 02 | 06 |  0.0114 |  0.0087\n",
      "00:01:22 02 | 07 | -0.0078 | -0.0063\n",
      "00:01:25 02 | 08 |  0.0179 |  0.0132\n",
      "00:01:28 02 | 09 |  0.0122 |  0.0209\n",
      "00:01:31 02 | 10 |  0.0067 |  0.0202\n",
      "00:01:34 02 | 11 |  0.0153 |  0.0272\n",
      "00:01:37 02 | 12 |  0.0172 |  0.0338\n",
      "00:01:40 02 | 13 |  0.0179 |  0.0274\n",
      "00:01:43 02 | 14 |  0.0122 |  0.0267\n",
      "00:01:46 02 | 15 |  0.0115 |  0.0158\n",
      "00:01:49 02 | 16 |  0.0184 |  0.0232\n",
      "00:01:52 02 | 17 |  0.0086 |  0.0042\n",
      "00:01:55 02 | 18 |  0.0159 |  0.0309\n",
      "00:01:58 02 | 19 |  0.0208 |  0.0267\n",
      "00:02:01 02 | 20 |  0.0169 |  0.0405\n",
      "00:02:05 03 | 01 | -0.0015 | -0.0070\n",
      "00:02:08 03 | 02 | -0.0204 | -0.0429\n",
      "00:02:11 03 | 03 | -0.0017 | -0.0280\n",
      "00:02:14 03 | 04 | -0.0206 | -0.0155\n",
      "00:02:17 03 | 05 |  0.0184 |  0.0054\n",
      "00:02:20 03 | 06 |  0.0019 | -0.0068\n",
      "00:02:23 03 | 07 | -0.0006 | -0.0219\n",
      "00:02:26 03 | 08 | -0.0012 | -0.0112\n",
      "00:02:29 03 | 09 | -0.0189 | -0.0229\n",
      "00:02:32 03 | 10 | -0.0065 | -0.0098\n",
      "00:02:35 03 | 11 | -0.0156 | -0.0407\n",
      "00:02:38 03 | 12 | -0.0221 | -0.0370\n",
      "00:02:41 03 | 13 |  0.0141 |  0.0067\n",
      "00:02:44 03 | 14 | -0.0138 | -0.0248\n",
      "00:02:47 03 | 15 |  0.0024 |  0.0022\n",
      "00:02:50 03 | 16 | -0.0005 | -0.0416\n",
      "00:02:53 03 | 17 | -0.0039 | -0.0181\n",
      "00:02:56 03 | 18 | -0.0018 | -0.0183\n",
      "00:02:59 03 | 19 |  0.0041 | -0.0279\n",
      "00:03:02 03 | 20 |  0.0133 |  0.0337\n",
      "00:03:05 04 | 01 |  0.0434 |  0.0398\n",
      "00:03:08 04 | 02 | -0.0536 | -0.0643\n",
      "00:03:11 04 | 03 |  0.0310 |  0.0358\n",
      "00:03:14 04 | 04 |  0.0183 |  0.0200\n",
      "00:03:17 04 | 05 |  0.0182 |  0.0013\n",
      "00:03:20 04 | 06 |  0.0268 |  0.0202\n",
      "00:03:23 04 | 07 |  0.0019 |  0.0011\n",
      "00:03:26 04 | 08 |  0.0035 | -0.0077\n",
      "00:03:29 04 | 09 | -0.0044 |  0.0084\n",
      "00:03:32 04 | 10 |  0.0105 | -0.0052\n",
      "00:03:35 04 | 11 |  0.0261 |  0.0170\n",
      "00:03:38 04 | 12 |  0.0259 |  0.0217\n",
      "00:03:41 04 | 13 | -0.0007 | -0.0174\n",
      "00:03:44 04 | 14 |  0.0030 |  0.0044\n",
      "00:03:47 04 | 15 | -0.0155 | -0.0441\n",
      "00:03:50 04 | 16 | -0.0088 | -0.0414\n",
      "00:03:53 04 | 17 | -0.0168 | -0.0455\n",
      "00:03:56 04 | 18 | -0.0156 | -0.0550\n",
      "00:03:59 04 | 19 | -0.0115 | -0.0330\n",
      "00:04:02 04 | 20 | -0.0067 | -0.0174\n",
      "00:04:06 05 | 01 |  0.0122 | -0.0008\n",
      "00:04:09 05 | 02 |  0.0258 |  0.0238\n",
      "00:04:12 05 | 03 | -0.0146 | -0.0002\n",
      "00:04:15 05 | 04 |  0.0349 |  0.0316\n",
      "00:04:18 05 | 05 | -0.0321 | -0.0337\n",
      "00:04:21 05 | 06 | -0.0042 | -0.0069\n",
      "00:04:24 05 | 07 |  0.0252 |  0.0459\n",
      "00:04:27 05 | 08 |  0.0032 |  0.0149\n",
      "00:04:30 05 | 09 |  0.0073 |  0.0193\n",
      "00:04:33 05 | 10 | -0.0000 | -0.0090\n",
      "00:04:36 05 | 11 |  0.0078 | -0.0003\n",
      "00:04:39 05 | 12 |  0.0046 |  0.0183\n",
      "00:04:42 05 | 13 |  0.0000 | -0.0003\n",
      "00:04:45 05 | 14 | -0.0028 | -0.0050\n",
      "00:04:48 05 | 15 |  0.0018 | -0.0096\n",
      "00:04:51 05 | 16 |  0.0131 |  0.0223\n",
      "00:04:54 05 | 17 | -0.0001 | -0.0081\n",
      "00:04:57 05 | 18 |  0.0054 | -0.0075\n",
      "00:05:00 05 | 19 | -0.0044 | -0.0120\n",
      "00:05:03 05 | 20 | -0.0029 | -0.0032\n",
      "00:05:07 06 | 01 |  0.0155 |  0.0141\n",
      "00:05:10 06 | 02 |  0.0264 |  0.0508\n",
      "00:05:13 06 | 03 |  0.0083 |  0.0043\n",
      "00:05:16 06 | 04 |  0.0076 | -0.0028\n",
      "00:05:19 06 | 05 |  0.0028 |  0.0197\n",
      "00:05:22 06 | 06 |  0.0225 | -0.0038\n",
      "00:05:25 06 | 07 |  0.0180 |  0.0045\n",
      "00:05:28 06 | 08 |  0.0242 |  0.0275\n",
      "00:05:31 06 | 09 |  0.0351 |  0.0294\n",
      "00:05:34 06 | 10 |  0.0240 |  0.0344\n",
      "00:05:37 06 | 11 |  0.0318 |  0.0290\n",
      "00:05:40 06 | 12 |  0.0410 |  0.0543\n",
      "00:05:43 06 | 13 |  0.0230 |  0.0243\n",
      "00:05:46 06 | 14 |  0.0334 |  0.0413\n",
      "00:05:49 06 | 15 |  0.0140 |  0.0189\n",
      "00:05:52 06 | 16 |  0.0325 |  0.0122\n",
      "00:05:55 06 | 17 |  0.0273 |  0.0223\n",
      "00:05:58 06 | 18 |  0.0302 |  0.0311\n",
      "00:06:01 06 | 19 |  0.0236 |  0.0131\n",
      "00:06:04 06 | 20 |  0.0331 |  0.0248\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:06:07 07 | 01 | -0.0044 |  0.0188\n",
      "00:06:10 07 | 02 |  0.0333 |  0.0392\n",
      "00:06:13 07 | 03 |  0.0152 |  0.0300\n",
      "00:06:16 07 | 04 |  0.0314 |  0.0496\n",
      "00:06:19 07 | 05 |  0.0106 |  0.0135\n",
      "00:06:22 07 | 06 |  0.0122 |  0.0395\n",
      "00:06:25 07 | 07 |  0.0017 |  0.0474\n",
      "00:06:28 07 | 08 |  0.0157 |  0.0495\n",
      "00:06:31 07 | 09 |  0.0205 |  0.0177\n",
      "00:06:34 07 | 10 |  0.0070 |  0.0277\n",
      "00:06:37 07 | 11 |  0.0112 |  0.0063\n",
      "00:06:40 07 | 12 |  0.0191 |  0.0280\n",
      "00:06:43 07 | 13 |  0.0022 | -0.0010\n",
      "00:06:46 07 | 14 |  0.0010 |  0.0044\n",
      "00:06:49 07 | 15 |  0.0191 |  0.0284\n",
      "00:06:52 07 | 16 |  0.0299 |  0.0266\n",
      "00:06:55 07 | 17 |  0.0075 |  0.0178\n",
      "00:06:58 07 | 18 |  0.0261 |  0.0428\n",
      "00:07:01 07 | 19 |  0.0113 |  0.0198\n",
      "00:07:04 07 | 20 | -0.0003 |  0.0164\n",
      "00:07:08 08 | 01 | -0.0091 | -0.0161\n",
      "00:07:11 08 | 02 | -0.0102 |  0.0179\n",
      "00:07:14 08 | 03 |  0.0014 |  0.0197\n",
      "00:07:17 08 | 04 |  0.0143 |  0.0168\n",
      "00:07:20 08 | 05 |  0.0188 |  0.0027\n",
      "00:07:23 08 | 06 |  0.0224 |  0.0311\n",
      "00:07:26 08 | 07 |  0.0268 |  0.0021\n",
      "00:07:29 08 | 08 |  0.0134 |  0.0053\n",
      "00:07:32 08 | 09 |  0.0251 |  0.0328\n",
      "00:07:35 08 | 10 |  0.0205 |  0.0194\n",
      "00:07:38 08 | 11 |  0.0061 | -0.0015\n",
      "00:07:41 08 | 12 |  0.0024 |  0.0112\n",
      "00:07:44 08 | 13 |  0.0076 |  0.0131\n",
      "00:07:47 08 | 14 |  0.0000 | -0.0082\n",
      "00:07:50 08 | 15 |  0.0055 |  0.0038\n",
      "00:07:52 08 | 16 |  0.0101 |  0.0084\n",
      "00:07:55 08 | 17 |  0.0112 | -0.0001\n",
      "00:07:58 08 | 18 |  0.0189 |  0.0211\n",
      "00:08:01 08 | 19 |  0.0109 | -0.0029\n",
      "00:08:04 08 | 20 |  0.0142 |  0.0022\n",
      "00:08:08 09 | 01 | -0.0035 |  0.0044\n",
      "00:08:11 09 | 02 |  0.0092 |  0.0010\n",
      "00:08:14 09 | 03 |  0.0063 | -0.0119\n",
      "00:08:17 09 | 04 |  0.0004 | -0.0120\n",
      "00:08:20 09 | 05 |  0.0090 | -0.0105\n",
      "00:08:23 09 | 06 |  0.0070 |  0.0018\n",
      "00:08:26 09 | 07 | -0.0115 | -0.0201\n",
      "00:08:29 09 | 08 |  0.0074 |  0.0020\n",
      "00:08:32 09 | 09 |  0.0034 | -0.0083\n",
      "00:08:35 09 | 10 |  0.0073 | -0.0060\n",
      "00:08:38 09 | 11 | -0.0025 |  0.0036\n",
      "00:08:41 09 | 12 | -0.0052 |  0.0069\n",
      "00:08:44 09 | 13 |  0.0032 |  0.0078\n",
      "00:08:47 09 | 14 |  0.0077 |  0.0115\n",
      "00:08:50 09 | 15 | -0.0023 | -0.0006\n",
      "00:08:53 09 | 16 |  0.0138 |  0.0202\n",
      "00:08:56 09 | 17 | -0.0023 |  0.0036\n",
      "00:08:59 09 | 18 | -0.0007 | -0.0196\n",
      "00:09:02 09 | 19 | -0.0015 |  0.0005\n",
      "00:09:05 09 | 20 |  0.0010 |  0.0017\n",
      "00:09:09 10 | 01 | -0.0203 | -0.0284\n",
      "00:09:12 10 | 02 | -0.0018 | -0.0133\n",
      "00:09:15 10 | 03 | -0.0198 | -0.0394\n",
      "00:09:18 10 | 04 | -0.0121 | -0.0076\n",
      "00:09:21 10 | 05 | -0.0311 | -0.0172\n",
      "00:09:23 10 | 06 | -0.0246 | -0.0269\n",
      "00:09:27 10 | 07 |  0.0104 |  0.0048\n",
      "00:09:29 10 | 08 | -0.0286 | -0.0514\n",
      "00:09:32 10 | 09 | -0.0364 | -0.0635\n",
      "00:09:35 10 | 10 | -0.0361 | -0.0283\n",
      "00:09:38 10 | 11 | -0.0248 | -0.0281\n",
      "00:09:41 10 | 12 | -0.0341 | -0.0322\n",
      "00:09:44 10 | 13 | -0.0162 | -0.0454\n",
      "00:09:47 10 | 14 | -0.0365 | -0.0209\n",
      "00:09:50 10 | 15 | -0.0345 | -0.0220\n",
      "00:09:53 10 | 16 | -0.0151 | -0.0086\n",
      "00:09:56 10 | 17 | -0.0220 | -0.0134\n",
      "00:09:59 10 | 18 |  0.0012 |  0.0093\n",
      "00:10:02 10 | 19 |  0.0005 |  0.0050\n",
      "00:10:05 10 | 20 | -0.0166 | -0.0055\n",
      "00:10:09 11 | 01 | -0.0172 | -0.0211\n",
      "00:10:12 11 | 02 |  0.0218 |  0.0232\n",
      "00:10:15 11 | 03 |  0.0386 |  0.0435\n",
      "00:10:18 11 | 04 |  0.0224 |  0.0218\n",
      "00:10:21 11 | 05 |  0.0275 |  0.0253\n",
      "00:10:24 11 | 06 | -0.0066 | -0.0089\n",
      "00:10:27 11 | 07 |  0.0239 |  0.0258\n",
      "00:10:30 11 | 08 | -0.0029 |  0.0092\n",
      "00:10:33 11 | 09 |  0.0240 |  0.0119\n",
      "00:10:36 11 | 10 |  0.0305 |  0.0282\n",
      "00:10:39 11 | 11 |  0.0142 |  0.0071\n",
      "00:10:42 11 | 12 |  0.0261 |  0.0284\n",
      "00:10:45 11 | 13 |  0.0208 |  0.0153\n",
      "00:10:48 11 | 14 | -0.0115 | -0.0179\n",
      "00:10:51 11 | 15 |  0.0231 |  0.0237\n",
      "00:10:54 11 | 16 |  0.0015 |  0.0094\n",
      "00:10:57 11 | 17 |  0.0152 |  0.0113\n",
      "00:10:60 11 | 18 | -0.0055 | -0.0046\n",
      "00:11:03 11 | 19 | -0.0163 | -0.0020\n",
      "00:11:06 11 | 20 | -0.0081 |  0.0002\n",
      "00:11:09 12 | 01 |  0.0117 |  0.0148\n",
      "00:11:12 12 | 02 |  0.0197 |  0.0305\n",
      "00:11:15 12 | 03 |  0.0143 |  0.0144\n",
      "00:11:18 12 | 04 |  0.0259 |  0.0235\n",
      "00:11:21 12 | 05 |  0.0198 |  0.0101\n",
      "00:11:24 12 | 06 |  0.0032 |  0.0127\n",
      "00:11:27 12 | 07 | -0.0055 | -0.0049\n",
      "00:11:30 12 | 08 |  0.0034 |  0.0039\n",
      "00:11:33 12 | 09 | -0.0074 | -0.0328\n",
      "00:11:36 12 | 10 | -0.0108 | -0.0367\n",
      "00:11:39 12 | 11 |  0.0027 | -0.0021\n",
      "00:11:42 12 | 12 |  0.0005 | -0.0104\n",
      "00:11:45 12 | 13 |  0.0008 |  0.0028\n",
      "00:11:48 12 | 14 | -0.0001 | -0.0046\n",
      "00:11:51 12 | 15 | -0.0084 | -0.0027\n",
      "00:11:54 12 | 16 | -0.0003 |  0.0116\n",
      "00:11:57 12 | 17 | -0.0139 |  0.0007\n",
      "00:12:00 12 | 18 | -0.0015 |  0.0112\n",
      "00:12:03 12 | 19 | -0.0092 | -0.0170\n",
      "00:12:06 12 | 20 | -0.0055 | -0.0067\n",
      "(32, 32) tanh 0.1 64\n",
      "00:00:11 01 | 01 | -0.0153 | -0.0017\n",
      "00:00:21 01 | 02 |  0.0291 |  0.0364\n",
      "00:00:30 01 | 03 |  0.0051 |  0.0091\n",
      "00:00:40 01 | 04 |  0.0128 |  0.0167\n",
      "00:00:50 01 | 05 |  0.0247 |  0.0370\n",
      "00:01:00 01 | 06 |  0.0154 |  0.0241\n",
      "00:01:10 01 | 07 |  0.0233 |  0.0096\n",
      "00:01:20 01 | 08 |  0.0134 |  0.0002\n",
      "00:01:30 01 | 09 |  0.0059 |  0.0098\n",
      "00:01:39 01 | 10 |  0.0146 |  0.0210\n",
      "00:01:49 01 | 11 |  0.0111 |  0.0235\n",
      "00:01:59 01 | 12 |  0.0157 |  0.0219\n",
      "00:02:09 01 | 13 |  0.0137 |  0.0042\n",
      "00:02:19 01 | 14 | -0.0029 |  0.0005\n",
      "00:02:29 01 | 15 |  0.0040 | -0.0014\n",
      "00:02:39 01 | 16 |  0.0147 |  0.0022\n",
      "00:02:48 01 | 17 |  0.0080 |  0.0047\n",
      "00:02:58 01 | 18 | -0.0011 |  0.0063\n",
      "00:03:08 01 | 19 |  0.0010 |  0.0075\n",
      "00:03:18 01 | 20 | -0.0083 | -0.0076\n",
      "00:03:29 02 | 01 |  0.0229 |  0.0196\n",
      "00:03:38 02 | 02 |  0.0062 |  0.0256\n",
      "00:03:48 02 | 03 |  0.0082 |  0.0180\n",
      "00:03:58 02 | 04 |  0.0035 |  0.0051\n",
      "00:04:08 02 | 05 |  0.0000 |  0.0033\n",
      "00:04:18 02 | 06 |  0.0066 |  0.0149\n",
      "00:04:28 02 | 07 |  0.0173 |  0.0102\n",
      "00:04:37 02 | 08 |  0.0111 |  0.0105\n",
      "00:04:47 02 | 09 |  0.0101 |  0.0042\n",
      "00:04:57 02 | 10 |  0.0190 |  0.0173\n",
      "00:05:07 02 | 11 | -0.0050 | -0.0141\n",
      "00:05:17 02 | 12 |  0.0031 | -0.0013\n",
      "00:05:27 02 | 13 |  0.0114 |  0.0074\n",
      "00:05:37 02 | 14 | -0.0038 | -0.0035\n",
      "00:05:46 02 | 15 |  0.0091 |  0.0055\n",
      "00:05:56 02 | 16 |  0.0095 |  0.0298\n",
      "00:06:06 02 | 17 | -0.0168 | -0.0143\n",
      "00:06:16 02 | 18 |  0.0184 |  0.0310\n",
      "00:06:26 02 | 19 |  0.0014 |  0.0193\n",
      "00:06:36 02 | 20 |  0.0123 |  0.0148\n",
      "00:06:46 03 | 01 | -0.0142 | -0.0189\n",
      "00:06:56 03 | 02 | -0.0087 |  0.0069\n",
      "00:07:06 03 | 03 |  0.0123 |  0.0033\n",
      "00:07:16 03 | 04 |  0.0160 |  0.0018\n",
      "00:07:26 03 | 05 | -0.0187 | -0.0140\n",
      "00:07:35 03 | 06 |  0.0013 |  0.0044\n",
      "00:07:45 03 | 07 |  0.0006 | -0.0062\n",
      "00:07:55 03 | 08 |  0.0173 |  0.0138\n",
      "00:08:05 03 | 09 | -0.0161 | -0.0137\n",
      "00:08:15 03 | 10 | -0.0078 |  0.0076\n",
      "00:08:25 03 | 11 |  0.0004 |  0.0066\n",
      "00:08:34 03 | 12 | -0.0215 | -0.0339\n",
      "00:08:44 03 | 13 |  0.0081 |  0.0056\n",
      "00:08:54 03 | 14 | -0.0136 | -0.0192\n",
      "00:09:04 03 | 15 |  0.0215 |  0.0217\n",
      "00:09:14 03 | 16 | -0.0006 |  0.0020\n",
      "00:09:24 03 | 17 |  0.0276 |  0.0354\n",
      "00:09:33 03 | 18 | -0.0094 | -0.0072\n",
      "00:09:43 03 | 19 |  0.0072 |  0.0110\n",
      "00:09:53 03 | 20 |  0.0371 |  0.0291\n",
      "00:10:04 04 | 01 |  0.0215 |  0.0248\n",
      "00:10:13 04 | 02 |  0.0475 |  0.0370\n",
      "00:10:24 04 | 03 | -0.0166 | -0.0252\n",
      "00:10:33 04 | 04 | -0.0027 | -0.0004\n",
      "00:10:43 04 | 05 | -0.0060 | -0.0163\n",
      "00:10:53 04 | 06 |  0.0048 | -0.0075\n",
      "00:11:03 04 | 07 | -0.0035 |  0.0037\n",
      "00:11:13 04 | 08 |  0.0041 | -0.0083\n",
      "00:11:23 04 | 09 |  0.0286 |  0.0230\n",
      "00:11:32 04 | 10 |  0.0251 |  0.0262\n",
      "00:11:42 04 | 11 |  0.0201 |  0.0026\n",
      "00:11:52 04 | 12 |  0.0123 |  0.0042\n",
      "00:12:02 04 | 13 |  0.0070 | -0.0004\n",
      "00:12:12 04 | 14 |  0.0219 |  0.0169\n",
      "00:12:22 04 | 15 | -0.0054 | -0.0093\n",
      "00:12:32 04 | 16 |  0.0237 |  0.0397\n",
      "00:12:41 04 | 17 | -0.0352 | -0.0375\n",
      "00:12:51 04 | 18 |  0.0031 |  0.0014\n",
      "00:13:01 04 | 19 | -0.0139 | -0.0273\n",
      "00:13:11 04 | 20 |  0.0081 | -0.0069\n",
      "00:13:22 05 | 01 |  0.0301 |  0.0249\n",
      "00:13:32 05 | 02 |  0.0127 |  0.0116\n",
      "00:13:42 05 | 03 |  0.0043 |  0.0135\n",
      "00:13:51 05 | 04 | -0.0239 | -0.0105\n",
      "00:14:01 05 | 05 | -0.0030 | -0.0155\n",
      "00:14:11 05 | 06 |  0.0023 | -0.0061\n",
      "00:14:21 05 | 07 | -0.0020 | -0.0073\n",
      "00:14:31 05 | 08 |  0.0081 | -0.0004\n",
      "00:14:41 05 | 09 | -0.0126 | -0.0076\n",
      "00:14:51 05 | 10 | -0.0120 | -0.0141\n",
      "00:15:01 05 | 11 | -0.0035 |  0.0046\n",
      "00:15:11 05 | 12 | -0.0102 | -0.0039\n",
      "00:15:20 05 | 13 | -0.0098 | -0.0117\n",
      "00:15:30 05 | 14 | -0.0243 | -0.0156\n",
      "00:15:40 05 | 15 | -0.0143 | -0.0057\n",
      "00:15:50 05 | 16 | -0.0077 |  0.0059\n",
      "00:15:60 05 | 17 | -0.0159 | -0.0192\n",
      "00:16:10 05 | 18 | -0.0069 |  0.0027\n",
      "00:16:20 05 | 19 | -0.0003 |  0.0117\n",
      "00:16:30 05 | 20 | -0.0064 | -0.0077\n",
      "00:16:40 06 | 01 | -0.0019 | -0.0007\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:16:50 06 | 02 |  0.0140 |  0.0059\n",
      "00:16:60 06 | 03 |  0.0479 |  0.0403\n",
      "00:17:10 06 | 04 |  0.0218 |  0.0195\n",
      "00:17:20 06 | 05 |  0.0369 |  0.0312\n",
      "00:17:30 06 | 06 |  0.0485 |  0.0246\n",
      "00:17:40 06 | 07 |  0.0270 |  0.0253\n",
      "00:17:49 06 | 08 |  0.0435 |  0.0412\n",
      "00:17:59 06 | 09 |  0.0346 |  0.0115\n",
      "00:18:09 06 | 10 |  0.0269 |  0.0056\n",
      "00:18:19 06 | 11 |  0.0221 |  0.0271\n",
      "00:18:29 06 | 12 |  0.0417 |  0.0404\n",
      "00:18:39 06 | 13 |  0.0391 |  0.0211\n",
      "00:18:49 06 | 14 |  0.0199 |  0.0062\n",
      "00:18:59 06 | 15 |  0.0305 |  0.0196\n",
      "00:19:09 06 | 16 |  0.0205 |  0.0101\n",
      "00:19:19 06 | 17 |  0.0330 |  0.0370\n",
      "00:19:28 06 | 18 |  0.0201 |  0.0055\n",
      "00:19:38 06 | 19 |  0.0164 |  0.0158\n",
      "00:19:48 06 | 20 |  0.0329 |  0.0278\n",
      "00:19:59 07 | 01 |  0.0075 |  0.0089\n",
      "00:20:09 07 | 02 |  0.0365 |  0.0517\n",
      "00:20:19 07 | 03 | -0.0217 | -0.0222\n",
      "00:20:28 07 | 04 | -0.0216 |  0.0369\n",
      "00:20:38 07 | 05 |  0.0020 |  0.0198\n",
      "00:20:48 07 | 06 |  0.0154 |  0.0482\n",
      "00:20:58 07 | 07 |  0.0144 |  0.0387\n",
      "00:21:08 07 | 08 |  0.0343 |  0.0474\n",
      "00:21:18 07 | 09 | -0.0147 | -0.0052\n",
      "00:21:28 07 | 10 |  0.0191 |  0.0395\n",
      "00:21:38 07 | 11 |  0.0204 |  0.0412\n",
      "00:21:48 07 | 12 | -0.0099 | -0.0016\n",
      "00:21:57 07 | 13 |  0.0135 |  0.0430\n",
      "00:22:07 07 | 14 |  0.0041 |  0.0290\n",
      "00:22:17 07 | 15 |  0.0232 |  0.0504\n",
      "00:22:27 07 | 16 |  0.0238 |  0.0308\n",
      "00:22:37 07 | 17 |  0.0039 |  0.0178\n",
      "00:22:47 07 | 18 | -0.0011 |  0.0089\n",
      "00:22:57 07 | 19 | -0.0027 |  0.0319\n",
      "00:23:07 07 | 20 |  0.0042 |  0.0198\n",
      "00:23:17 08 | 01 |  0.0055 |  0.0182\n",
      "00:23:27 08 | 02 |  0.0073 | -0.0060\n",
      "00:23:37 08 | 03 | -0.0020 |  0.0018\n",
      "00:23:47 08 | 04 |  0.0131 |  0.0009\n",
      "00:23:57 08 | 05 | -0.0036 | -0.0161\n",
      "00:24:07 08 | 06 |  0.0034 |  0.0003\n",
      "00:24:16 08 | 07 |  0.0023 |  0.0011\n",
      "00:24:26 08 | 08 |  0.0394 |  0.0307\n",
      "00:24:36 08 | 09 | -0.0072 | -0.0079\n",
      "00:24:46 08 | 10 | -0.0020 |  0.0211\n",
      "00:24:56 08 | 11 |  0.0093 |  0.0448\n",
      "00:25:06 08 | 12 |  0.0181 |  0.0151\n",
      "00:25:16 08 | 13 |  0.0136 |  0.0073\n",
      "00:25:25 08 | 14 | -0.0094 | -0.0118\n",
      "00:25:35 08 | 15 |  0.0003 | -0.0018\n",
      "00:25:45 08 | 16 |  0.0091 |  0.0063\n",
      "00:25:55 08 | 17 |  0.0043 |  0.0009\n",
      "00:26:05 08 | 18 | -0.0107 | -0.0152\n",
      "00:26:15 08 | 19 |  0.0087 | -0.0013\n",
      "00:26:24 08 | 20 |  0.0034 |  0.0161\n",
      "00:26:35 09 | 01 | -0.0035 |  0.0055\n",
      "00:26:45 09 | 02 |  0.0304 |  0.0375\n",
      "00:26:55 09 | 03 |  0.0141 |  0.0102\n",
      "00:27:04 09 | 04 |  0.0285 |  0.0125\n",
      "00:27:14 09 | 05 |  0.0139 |  0.0171\n",
      "00:27:24 09 | 06 |  0.0294 |  0.0164\n",
      "00:27:34 09 | 07 |  0.0152 |  0.0064\n",
      "00:27:44 09 | 08 | -0.0014 |  0.0092\n",
      "00:27:54 09 | 09 | -0.0029 | -0.0135\n",
      "00:28:04 09 | 10 | -0.0081 | -0.0129\n",
      "00:28:13 09 | 11 |  0.0072 |  0.0037\n",
      "00:28:23 09 | 12 |  0.0206 |  0.0246\n",
      "00:28:33 09 | 13 |  0.0118 |  0.0087\n",
      "00:28:43 09 | 14 |  0.0079 |  0.0130\n",
      "00:28:53 09 | 15 |  0.0190 |  0.0035\n",
      "00:29:03 09 | 16 | -0.0037 | -0.0163\n",
      "00:29:12 09 | 17 |  0.0040 | -0.0084\n",
      "00:29:22 09 | 18 |  0.0142 |  0.0060\n",
      "00:29:32 09 | 19 |  0.0098 | -0.0017\n",
      "00:29:42 09 | 20 |  0.0018 |  0.0125\n",
      "00:29:52 10 | 01 | -0.0098 | -0.0091\n",
      "00:30:02 10 | 02 |  0.0207 |  0.0234\n",
      "00:30:12 10 | 03 |  0.0236 |  0.0191\n",
      "00:30:22 10 | 04 | -0.0135 | -0.0123\n",
      "00:30:32 10 | 05 | -0.0092 | -0.0173\n",
      "00:30:41 10 | 06 | -0.0189 | -0.0135\n",
      "00:30:51 10 | 07 | -0.0065 |  0.0008\n",
      "00:31:01 10 | 08 | -0.0061 |  0.0090\n",
      "00:31:11 10 | 09 | -0.0045 |  0.0124\n",
      "00:31:21 10 | 10 | -0.0009 | -0.0106\n",
      "00:31:31 10 | 11 |  0.0127 |  0.0221\n",
      "00:31:40 10 | 12 | -0.0153 | -0.0136\n",
      "00:31:50 10 | 13 | -0.0007 | -0.0016\n",
      "00:32:00 10 | 14 | -0.0077 | -0.0015\n",
      "00:32:10 10 | 15 | -0.0078 | -0.0317\n",
      "00:32:20 10 | 16 |  0.0071 |  0.0074\n",
      "00:32:30 10 | 17 |  0.0058 |  0.0087\n",
      "00:32:39 10 | 18 | -0.0087 | -0.0378\n",
      "00:32:49 10 | 19 |  0.0030 | -0.0098\n",
      "00:32:59 10 | 20 | -0.0007 | -0.0088\n",
      "00:33:09 11 | 01 |  0.0183 |  0.0013\n",
      "00:33:19 11 | 02 |  0.0130 |  0.0310\n",
      "00:33:29 11 | 03 |  0.0325 |  0.0330\n",
      "00:33:39 11 | 04 |  0.0005 | -0.0034\n",
      "00:33:49 11 | 05 |  0.0045 | -0.0045\n",
      "00:33:59 11 | 06 |  0.0181 |  0.0196\n",
      "00:34:09 11 | 07 | -0.0020 |  0.0024\n",
      "00:34:18 11 | 08 |  0.0095 |  0.0090\n",
      "00:34:28 11 | 09 | -0.0079 | -0.0054\n",
      "00:34:38 11 | 10 |  0.0193 |  0.0153\n",
      "00:34:48 11 | 11 |  0.0199 |  0.0244\n",
      "00:34:58 11 | 12 | -0.0040 |  0.0035\n",
      "00:35:07 11 | 13 |  0.0096 |  0.0090\n",
      "00:35:17 11 | 14 |  0.0082 | -0.0055\n",
      "00:35:27 11 | 15 |  0.0062 |  0.0172\n",
      "00:35:37 11 | 16 | -0.0062 | -0.0146\n",
      "00:35:47 11 | 17 |  0.0025 |  0.0023\n",
      "00:35:57 11 | 18 | -0.0078 | -0.0001\n",
      "00:36:06 11 | 19 |  0.0057 |  0.0176\n",
      "00:36:16 11 | 20 | -0.0217 | -0.0293\n",
      "00:36:27 12 | 01 | -0.0046 | -0.0016\n",
      "00:36:37 12 | 02 |  0.0177 |  0.0020\n",
      "00:36:46 12 | 03 |  0.0039 | -0.0071\n",
      "00:36:56 12 | 04 | -0.0078 | -0.0008\n",
      "00:37:06 12 | 05 |  0.0052 | -0.0183\n",
      "00:37:16 12 | 06 | -0.0071 | -0.0033\n",
      "00:37:26 12 | 07 | -0.0077 | -0.0246\n",
      "00:37:36 12 | 08 | -0.0014 |  0.0029\n",
      "00:37:46 12 | 09 |  0.0032 |  0.0067\n",
      "00:37:56 12 | 10 |  0.0071 | -0.0267\n",
      "00:38:05 12 | 11 |  0.0072 |  0.0201\n",
      "00:38:15 12 | 12 | -0.0058 |  0.0017\n",
      "00:38:25 12 | 13 | -0.0037 | -0.0271\n",
      "00:38:35 12 | 14 | -0.0040 |  0.0098\n",
      "00:38:45 12 | 15 | -0.0093 | -0.0027\n",
      "00:38:55 12 | 16 | -0.0102 | -0.0080\n",
      "00:39:05 12 | 17 | -0.0076 | -0.0136\n",
      "00:39:15 12 | 18 | -0.0085 | -0.0077\n",
      "00:39:25 12 | 19 | -0.0263 | -0.0113\n",
      "00:39:34 12 | 20 | -0.0222 | -0.0257\n",
      "(32, 32) tanh 0.1 256\n",
      "00:00:04 01 | 01 |  0.0077 | -0.0021\n",
      "00:00:07 01 | 02 |  0.0110 | -0.0012\n",
      "00:00:10 01 | 03 |  0.0021 | -0.0069\n",
      "00:00:13 01 | 04 |  0.0219 |  0.0137\n",
      "00:00:16 01 | 05 |  0.0167 |  0.0005\n",
      "00:00:19 01 | 06 |  0.0088 | -0.0100\n",
      "00:00:22 01 | 07 |  0.0203 |  0.0251\n",
      "00:00:25 01 | 08 |  0.0262 |  0.0324\n",
      "00:00:28 01 | 09 |  0.0094 |  0.0067\n",
      "00:00:31 01 | 10 |  0.0039 |  0.0173\n",
      "00:00:34 01 | 11 |  0.0081 |  0.0138\n",
      "00:00:37 01 | 12 |  0.0213 |  0.0189\n",
      "00:00:40 01 | 13 |  0.0148 |  0.0096\n",
      "00:00:43 01 | 14 |  0.0176 |  0.0326\n",
      "00:00:46 01 | 15 |  0.0035 |  0.0111\n",
      "00:00:49 01 | 16 |  0.0131 |  0.0129\n",
      "00:00:52 01 | 17 |  0.0214 |  0.0258\n",
      "00:00:55 01 | 18 |  0.0177 |  0.0296\n",
      "00:00:58 01 | 19 |  0.0196 |  0.0257\n",
      "00:01:01 01 | 20 |  0.0147 |  0.0207\n",
      "00:01:04 02 | 01 |  0.0042 |  0.0058\n",
      "00:01:07 02 | 02 |  0.0019 |  0.0116\n",
      "00:01:10 02 | 03 | -0.0120 |  0.0026\n",
      "00:01:13 02 | 04 | -0.0156 | -0.0184\n",
      "00:01:16 02 | 05 |  0.0077 |  0.0048\n",
      "00:01:19 02 | 06 |  0.0139 |  0.0088\n",
      "00:01:22 02 | 07 | -0.0121 | -0.0200\n",
      "00:01:25 02 | 08 |  0.0083 |  0.0069\n",
      "00:01:28 02 | 09 | -0.0024 |  0.0044\n",
      "00:01:31 02 | 10 | -0.0133 | -0.0167\n",
      "00:01:34 02 | 11 |  0.0062 |  0.0170\n",
      "00:01:37 02 | 12 |  0.0036 |  0.0087\n",
      "00:01:40 02 | 13 | -0.0139 | -0.0125\n",
      "00:01:43 02 | 14 |  0.0083 |  0.0112\n",
      "00:01:46 02 | 15 |  0.0058 |  0.0053\n",
      "00:01:49 02 | 16 | -0.0053 |  0.0001\n",
      "00:01:52 02 | 17 |  0.0036 |  0.0009\n",
      "00:01:55 02 | 18 | -0.0025 | -0.0003\n",
      "00:01:58 02 | 19 |  0.0061 | -0.0054\n",
      "00:02:01 02 | 20 |  0.0093 |  0.0026\n",
      "00:02:05 03 | 01 | -0.0209 | -0.0375\n",
      "00:02:08 03 | 02 |  0.0036 | -0.0007\n",
      "00:02:11 03 | 03 | -0.0014 | -0.0033\n",
      "00:02:14 03 | 04 | -0.0097 |  0.0041\n",
      "00:02:17 03 | 05 |  0.0142 |  0.0150\n",
      "00:02:20 03 | 06 |  0.0142 | -0.0103\n",
      "00:02:23 03 | 07 |  0.0168 |  0.0088\n",
      "00:02:26 03 | 08 |  0.0143 | -0.0099\n",
      "00:02:29 03 | 09 | -0.0058 | -0.0175\n",
      "00:02:32 03 | 10 |  0.0073 |  0.0178\n",
      "00:02:35 03 | 11 |  0.0089 | -0.0108\n",
      "00:02:38 03 | 12 |  0.0227 |  0.0224\n",
      "00:02:41 03 | 13 |  0.0215 |  0.0294\n",
      "00:02:44 03 | 14 |  0.0103 |  0.0159\n",
      "00:02:47 03 | 15 |  0.0177 |  0.0230\n",
      "00:02:50 03 | 16 |  0.0110 |  0.0205\n",
      "00:02:53 03 | 17 |  0.0040 | -0.0101\n",
      "00:02:55 03 | 18 |  0.0053 | -0.0101\n",
      "00:02:58 03 | 19 |  0.0087 |  0.0117\n",
      "00:03:01 03 | 20 |  0.0081 | -0.0235\n",
      "00:03:05 04 | 01 |  0.0277 |  0.0221\n",
      "00:03:08 04 | 02 |  0.0254 |  0.0154\n",
      "00:03:11 04 | 03 | -0.0084 |  0.0069\n",
      "00:03:14 04 | 04 |  0.0250 |  0.0213\n",
      "00:03:17 04 | 05 |  0.0118 |  0.0035\n",
      "00:03:20 04 | 06 | -0.0060 | -0.0212\n",
      "00:03:23 04 | 07 |  0.0214 |  0.0116\n",
      "00:03:26 04 | 08 | -0.0008 | -0.0141\n",
      "00:03:29 04 | 09 |  0.0165 |  0.0010\n",
      "00:03:32 04 | 10 | -0.0081 | -0.0232\n",
      "00:03:35 04 | 11 |  0.0351 |  0.0240\n",
      "00:03:38 04 | 12 | -0.0115 | -0.0219\n",
      "00:03:41 04 | 13 |  0.0344 |  0.0279\n",
      "00:03:44 04 | 14 |  0.0259 |  0.0206\n",
      "00:03:47 04 | 15 |  0.0193 |  0.0275\n",
      "00:03:50 04 | 16 |  0.0045 |  0.0014\n",
      "00:03:53 04 | 17 |  0.0029 | -0.0148\n",
      "00:03:56 04 | 18 |  0.0120 |  0.0021\n",
      "00:03:59 04 | 19 |  0.0219 |  0.0200\n",
      "00:04:02 04 | 20 |  0.0242 |  0.0225\n",
      "00:04:06 05 | 01 |  0.0092 | -0.0056\n",
      "00:04:08 05 | 02 |  0.0053 |  0.0083\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:04:11 05 | 03 |  0.0113 |  0.0105\n",
      "00:04:14 05 | 04 |  0.0297 |  0.0296\n",
      "00:04:17 05 | 05 | -0.0027 | -0.0140\n",
      "00:04:20 05 | 06 |  0.0021 |  0.0162\n",
      "00:04:23 05 | 07 |  0.0041 |  0.0096\n",
      "00:04:26 05 | 08 |  0.0068 |  0.0211\n",
      "00:04:29 05 | 09 |  0.0272 |  0.0551\n",
      "00:04:32 05 | 10 |  0.0139 |  0.0204\n",
      "00:04:35 05 | 11 |  0.0171 |  0.0196\n",
      "00:04:38 05 | 12 |  0.0075 |  0.0114\n",
      "00:04:41 05 | 13 |  0.0088 |  0.0246\n",
      "00:04:44 05 | 14 |  0.0087 |  0.0250\n",
      "00:04:47 05 | 15 |  0.0067 | -0.0009\n",
      "00:04:50 05 | 16 |  0.0180 |  0.0080\n",
      "00:04:53 05 | 17 |  0.0159 |  0.0179\n",
      "00:04:56 05 | 18 | -0.0040 | -0.0026\n",
      "00:04:59 05 | 19 |  0.0127 |  0.0173\n",
      "00:05:02 05 | 20 | -0.0012 | -0.0032\n",
      "00:05:06 06 | 01 |  0.0071 | -0.0029\n",
      "00:05:09 06 | 02 |  0.0417 |  0.0564\n",
      "00:05:12 06 | 03 |  0.0092 |  0.0245\n",
      "00:05:15 06 | 04 |  0.0166 |  0.0197\n",
      "00:05:18 06 | 05 |  0.0339 |  0.0074\n",
      "00:05:21 06 | 06 |  0.0339 |  0.0293\n",
      "00:05:24 06 | 07 |  0.0375 |  0.0226\n",
      "00:05:27 06 | 08 |  0.0398 |  0.0148\n",
      "00:05:30 06 | 09 |  0.0521 |  0.0479\n",
      "00:05:33 06 | 10 |  0.0248 |  0.0257\n",
      "00:05:36 06 | 11 |  0.0441 |  0.0427\n",
      "00:05:39 06 | 12 |  0.0384 |  0.0313\n",
      "00:05:42 06 | 13 |  0.0354 |  0.0359\n",
      "00:05:45 06 | 14 |  0.0298 |  0.0320\n",
      "00:05:48 06 | 15 |  0.0363 |  0.0448\n",
      "00:05:51 06 | 16 |  0.0315 |  0.0313\n",
      "00:05:54 06 | 17 |  0.0320 |  0.0518\n",
      "00:05:57 06 | 18 |  0.0249 |  0.0141\n",
      "00:05:60 06 | 19 |  0.0214 |  0.0145\n",
      "00:06:03 06 | 20 |  0.0130 |  0.0254\n",
      "00:06:06 07 | 01 |  0.0082 |  0.0299\n",
      "00:06:09 07 | 02 |  0.0202 |  0.0167\n",
      "00:06:12 07 | 03 |  0.0056 |  0.0159\n",
      "00:06:15 07 | 04 | -0.0048 |  0.0083\n",
      "00:06:18 07 | 05 |  0.0465 |  0.0579\n",
      "00:06:21 07 | 06 |  0.0237 |  0.0286\n",
      "00:06:24 07 | 07 |  0.0225 |  0.0385\n",
      "00:06:27 07 | 08 | -0.0018 |  0.0070\n",
      "00:06:30 07 | 09 |  0.0220 |  0.0050\n",
      "00:06:33 07 | 10 |  0.0286 |  0.0325\n",
      "00:06:36 07 | 11 |  0.0331 |  0.0498\n",
      "00:06:39 07 | 12 |  0.0415 |  0.0309\n",
      "00:06:42 07 | 13 |  0.0380 |  0.0303\n",
      "00:06:45 07 | 14 |  0.0472 |  0.0411\n",
      "00:06:48 07 | 15 |  0.0391 |  0.0180\n",
      "00:06:51 07 | 16 |  0.0524 |  0.0513\n",
      "00:06:54 07 | 17 |  0.0421 |  0.0532\n",
      "00:06:57 07 | 18 |  0.0361 |  0.0512\n",
      "00:06:60 07 | 19 |  0.0417 |  0.0368\n",
      "00:07:03 07 | 20 |  0.0281 |  0.0294\n",
      "00:07:07 08 | 01 |  0.0136 |  0.0166\n",
      "00:07:10 08 | 02 | -0.0105 | -0.0202\n",
      "00:07:13 08 | 03 |  0.0028 | -0.0161\n",
      "00:07:16 08 | 04 |  0.0174 |  0.0308\n",
      "00:07:19 08 | 05 |  0.0101 |  0.0097\n",
      "00:07:22 08 | 06 |  0.0284 |  0.0208\n",
      "00:07:25 08 | 07 |  0.0078 |  0.0393\n",
      "00:07:28 08 | 08 |  0.0214 |  0.0230\n",
      "00:07:31 08 | 09 |  0.0199 |  0.0389\n",
      "00:07:34 08 | 10 |  0.0120 |  0.0369\n",
      "00:07:37 08 | 11 |  0.0169 |  0.0257\n",
      "00:07:40 08 | 12 |  0.0188 |  0.0238\n",
      "00:07:43 08 | 13 |  0.0250 |  0.0251\n",
      "00:07:46 08 | 14 |  0.0233 |  0.0296\n",
      "00:07:49 08 | 15 |  0.0272 |  0.0251\n",
      "00:07:52 08 | 16 |  0.0190 |  0.0214\n",
      "00:07:55 08 | 17 |  0.0225 |  0.0103\n",
      "00:07:58 08 | 18 |  0.0227 |  0.0088\n",
      "00:08:01 08 | 19 |  0.0222 |  0.0247\n",
      "00:08:04 08 | 20 |  0.0146 |  0.0269\n",
      "00:08:07 09 | 01 | -0.0131 | -0.0042\n",
      "00:08:10 09 | 02 |  0.0202 |  0.0261\n",
      "00:08:13 09 | 03 |  0.0225 |  0.0243\n",
      "00:08:16 09 | 04 |  0.0180 |  0.0269\n",
      "00:08:19 09 | 05 |  0.0095 |  0.0102\n",
      "00:08:22 09 | 06 | -0.0034 | -0.0082\n",
      "00:08:25 09 | 07 |  0.0020 | -0.0099\n",
      "00:08:28 09 | 08 |  0.0070 |  0.0122\n",
      "00:08:31 09 | 09 |  0.0080 |  0.0120\n",
      "00:08:34 09 | 10 |  0.0117 |  0.0086\n",
      "00:08:37 09 | 11 |  0.0100 |  0.0197\n",
      "00:08:40 09 | 12 |  0.0070 |  0.0082\n",
      "00:08:43 09 | 13 | -0.0072 | -0.0007\n",
      "00:08:46 09 | 14 |  0.0059 |  0.0163\n",
      "00:08:49 09 | 15 |  0.0039 |  0.0086\n",
      "00:08:52 09 | 16 |  0.0018 |  0.0015\n",
      "00:08:55 09 | 17 | -0.0032 | -0.0039\n",
      "00:08:58 09 | 18 |  0.0131 |  0.0111\n",
      "00:09:01 09 | 19 |  0.0128 |  0.0054\n",
      "00:09:04 09 | 20 |  0.0110 |  0.0155\n",
      "00:09:08 10 | 01 | -0.0321 | -0.0442\n",
      "00:09:11 10 | 02 |  0.0305 |  0.0324\n",
      "00:09:14 10 | 03 | -0.0049 | -0.0099\n",
      "00:09:17 10 | 04 | -0.0162 | -0.0074\n",
      "00:09:20 10 | 05 |  0.0249 |  0.0169\n",
      "00:09:23 10 | 06 | -0.0185 | -0.0114\n",
      "00:09:26 10 | 07 |  0.0004 |  0.0115\n",
      "00:09:29 10 | 08 |  0.0106 |  0.0333\n",
      "00:09:32 10 | 09 | -0.0158 | -0.0081\n",
      "00:09:35 10 | 10 | -0.0157 | -0.0109\n",
      "00:09:38 10 | 11 | -0.0199 | -0.0050\n",
      "00:09:41 10 | 12 |  0.0240 |  0.0237\n",
      "00:09:44 10 | 13 | -0.0017 |  0.0014\n",
      "00:09:47 10 | 14 | -0.0082 |  0.0029\n",
      "00:09:50 10 | 15 | -0.0258 | -0.0131\n",
      "00:09:53 10 | 16 |  0.0003 | -0.0205\n",
      "00:09:56 10 | 17 | -0.0279 | -0.0303\n",
      "00:09:59 10 | 18 | -0.0275 | -0.0298\n",
      "00:10:02 10 | 19 | -0.0446 | -0.0436\n",
      "00:10:05 10 | 20 | -0.0099 |  0.0019\n",
      "00:10:08 11 | 01 |  0.0145 |  0.0202\n",
      "00:10:11 11 | 02 |  0.0413 |  0.0528\n",
      "00:10:14 11 | 03 |  0.0151 |  0.0155\n",
      "00:10:17 11 | 04 |  0.0261 |  0.0354\n",
      "00:10:20 11 | 05 | -0.0006 |  0.0047\n",
      "00:10:23 11 | 06 |  0.0123 | -0.0004\n",
      "00:10:26 11 | 07 |  0.0144 |  0.0216\n",
      "00:10:29 11 | 08 |  0.0036 |  0.0045\n",
      "00:10:32 11 | 09 |  0.0040 | -0.0035\n",
      "00:10:35 11 | 10 |  0.0147 |  0.0063\n",
      "00:10:38 11 | 11 |  0.0264 |  0.0383\n",
      "00:10:41 11 | 12 |  0.0210 |  0.0187\n",
      "00:10:44 11 | 13 |  0.0391 |  0.0457\n",
      "00:10:47 11 | 14 |  0.0149 |  0.0025\n",
      "00:10:50 11 | 15 |  0.0168 |  0.0165\n",
      "00:10:53 11 | 16 |  0.0037 |  0.0080\n",
      "00:10:56 11 | 17 | -0.0085 | -0.0142\n",
      "00:10:59 11 | 18 |  0.0133 |  0.0107\n",
      "00:11:02 11 | 19 |  0.0144 |  0.0128\n",
      "00:11:05 11 | 20 |  0.0208 |  0.0188\n",
      "00:11:09 12 | 01 |  0.0012 |  0.0105\n",
      "00:11:12 12 | 02 |  0.0208 |  0.0223\n",
      "00:11:15 12 | 03 |  0.0285 |  0.0227\n",
      "00:11:18 12 | 04 |  0.0335 |  0.0563\n",
      "00:11:21 12 | 05 |  0.0047 |  0.0078\n",
      "00:11:24 12 | 06 | -0.0067 | -0.0185\n",
      "00:11:27 12 | 07 | -0.0195 | -0.0228\n",
      "00:11:30 12 | 08 | -0.0005 |  0.0005\n",
      "00:11:33 12 | 09 |  0.0092 |  0.0141\n",
      "00:11:36 12 | 10 | -0.0021 | -0.0266\n",
      "00:11:39 12 | 11 | -0.0036 |  0.0021\n",
      "00:11:42 12 | 12 | -0.0018 | -0.0012\n",
      "00:11:45 12 | 13 |  0.0023 |  0.0066\n",
      "00:11:48 12 | 14 |  0.0008 | -0.0006\n",
      "00:11:51 12 | 15 | -0.0031 |  0.0091\n",
      "00:11:54 12 | 16 |  0.0008 | -0.0040\n",
      "00:11:57 12 | 17 | -0.0063 |  0.0099\n",
      "00:11:60 12 | 18 | -0.0092 |  0.0049\n",
      "00:12:03 12 | 19 | -0.0031 | -0.0046\n",
      "00:12:06 12 | 20 | -0.0015 |  0.0083\n",
      "(32, 16) tanh 0 64\n",
      "00:00:11 01 | 01 | -0.0051 | -0.0111\n",
      "00:00:20 01 | 02 |  0.0010 |  0.0009\n",
      "00:00:30 01 | 03 |  0.0091 | -0.0033\n",
      "00:00:40 01 | 04 |  0.0115 |  0.0142\n",
      "00:00:49 01 | 05 |  0.0108 |  0.0015\n",
      "00:00:59 01 | 06 |  0.0087 |  0.0096\n",
      "00:01:08 01 | 07 |  0.0100 |  0.0021\n",
      "00:01:18 01 | 08 |  0.0111 |  0.0007\n",
      "00:01:28 01 | 09 |  0.0113 | -0.0011\n",
      "00:01:37 01 | 10 |  0.0052 |  0.0097\n",
      "00:01:47 01 | 11 |  0.0111 |  0.0016\n",
      "00:01:57 01 | 12 |  0.0100 | -0.0024\n",
      "00:02:06 01 | 13 |  0.0082 |  0.0092\n",
      "00:02:16 01 | 14 |  0.0086 |  0.0109\n",
      "00:02:25 01 | 15 |  0.0133 |  0.0073\n",
      "00:02:35 01 | 16 |  0.0093 |  0.0039\n",
      "00:02:45 01 | 17 |  0.0121 |  0.0143\n",
      "00:02:54 01 | 18 |  0.0097 | -0.0050\n",
      "00:03:04 01 | 19 |  0.0098 |  0.0008\n",
      "00:03:14 01 | 20 |  0.0098 |  0.0059\n",
      "00:03:24 02 | 01 |  0.0029 |  0.0048\n",
      "00:03:34 02 | 02 | -0.0151 | -0.0224\n",
      "00:03:43 02 | 03 | -0.0118 | -0.0085\n",
      "00:03:53 02 | 04 |  0.0044 |  0.0089\n",
      "00:04:03 02 | 05 |  0.0072 |  0.0153\n",
      "00:04:13 02 | 06 |  0.0196 |  0.0248\n",
      "00:04:22 02 | 07 |  0.0112 |  0.0288\n",
      "00:04:32 02 | 08 |  0.0114 |  0.0152\n",
      "00:04:42 02 | 09 | -0.0057 | -0.0160\n",
      "00:04:52 02 | 10 | -0.0014 | -0.0079\n",
      "00:05:01 02 | 11 | -0.0037 |  0.0007\n",
      "00:05:11 02 | 12 | -0.0088 |  0.0016\n",
      "00:05:21 02 | 13 |  0.0051 |  0.0276\n",
      "00:05:30 02 | 14 |  0.0089 |  0.0126\n",
      "00:05:40 02 | 15 |  0.0104 |  0.0006\n",
      "00:05:50 02 | 16 | -0.0003 |  0.0227\n",
      "00:05:60 02 | 17 | -0.0033 |  0.0147\n",
      "00:06:09 02 | 18 |  0.0092 |  0.0131\n",
      "00:06:19 02 | 19 | -0.0081 | -0.0122\n",
      "00:06:29 02 | 20 |  0.0071 |  0.0261\n",
      "00:06:39 03 | 01 |  0.0255 |  0.0312\n",
      "00:06:49 03 | 02 |  0.0289 |  0.0103\n",
      "00:06:59 03 | 03 |  0.0181 |  0.0089\n",
      "00:07:08 03 | 04 | -0.0185 | -0.0432\n",
      "00:07:18 03 | 05 | -0.0091 | -0.0068\n",
      "00:07:28 03 | 06 |  0.0264 |  0.0349\n",
      "00:07:38 03 | 07 |  0.0358 |  0.0268\n",
      "00:07:47 03 | 08 |  0.0271 |  0.0361\n",
      "00:07:57 03 | 09 | -0.0194 | -0.0348\n",
      "00:08:07 03 | 10 | -0.0224 | -0.0456\n",
      "00:08:17 03 | 11 |  0.0167 |  0.0113\n",
      "00:08:26 03 | 12 | -0.0246 | -0.0381\n",
      "00:08:36 03 | 13 | -0.0228 | -0.0335\n",
      "00:08:46 03 | 14 | -0.0096 | -0.0109\n",
      "00:08:56 03 | 15 |  0.0232 |  0.0278\n",
      "00:09:06 03 | 16 |  0.0115 |  0.0293\n",
      "00:09:15 03 | 17 |  0.0254 |  0.0272\n",
      "00:09:25 03 | 18 | -0.0129 | -0.0273\n",
      "00:09:35 03 | 19 |  0.0226 |  0.0121\n",
      "00:09:45 03 | 20 |  0.0281 |  0.0173\n",
      "00:09:55 04 | 01 | -0.0091 | -0.0177\n",
      "00:10:05 04 | 02 | -0.0215 | -0.0302\n",
      "00:10:15 04 | 03 |  0.0045 | -0.0137\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:10:24 04 | 04 | -0.0285 | -0.0558\n",
      "00:10:34 04 | 05 | -0.0280 | -0.0369\n",
      "00:10:44 04 | 06 | -0.0001 |  0.0104\n",
      "00:10:53 04 | 07 |  0.0007 |  0.0047\n",
      "00:11:03 04 | 08 |  0.0049 |  0.0003\n",
      "00:11:13 04 | 09 | -0.0144 |  0.0096\n",
      "00:11:22 04 | 10 |  0.0128 |  0.0069\n",
      "00:11:32 04 | 11 | -0.0062 |  0.0093\n",
      "00:11:42 04 | 12 | -0.0015 |  0.0049\n",
      "00:11:51 04 | 13 | -0.0196 | -0.0214\n",
      "00:12:01 04 | 14 | -0.0075 | -0.0095\n",
      "00:12:11 04 | 15 |  0.0231 |  0.0249\n",
      "00:12:20 04 | 16 | -0.0100 |  0.0056\n",
      "00:12:30 04 | 17 | -0.0137 | -0.0165\n",
      "00:12:40 04 | 18 | -0.0229 | -0.0230\n",
      "00:12:50 04 | 19 | -0.0038 |  0.0173\n",
      "00:12:59 04 | 20 | -0.0424 | -0.0223\n",
      "00:13:10 05 | 01 | -0.0102 |  0.0132\n",
      "00:13:19 05 | 02 | -0.0219 | -0.0350\n",
      "00:13:29 05 | 03 |  0.0106 |  0.0176\n",
      "00:13:39 05 | 04 |  0.0269 |  0.0133\n",
      "00:13:48 05 | 05 |  0.0211 |  0.0139\n",
      "00:13:58 05 | 06 |  0.0230 |  0.0303\n",
      "00:14:08 05 | 07 |  0.0060 |  0.0063\n",
      "00:14:18 05 | 08 |  0.0011 | -0.0009\n",
      "00:14:27 05 | 09 |  0.0210 |  0.0235\n",
      "00:14:37 05 | 10 |  0.0093 |  0.0161\n",
      "00:14:47 05 | 11 |  0.0052 |  0.0279\n",
      "00:14:57 05 | 12 |  0.0042 |  0.0036\n",
      "00:15:06 05 | 13 | -0.0060 | -0.0011\n",
      "00:15:16 05 | 14 | -0.0183 | -0.0231\n",
      "00:15:26 05 | 15 | -0.0024 | -0.0079\n",
      "00:15:35 05 | 16 | -0.0074 | -0.0189\n",
      "00:15:45 05 | 17 | -0.0070 | -0.0147\n",
      "00:15:55 05 | 18 | -0.0156 | -0.0210\n",
      "00:16:05 05 | 19 | -0.0205 | -0.0134\n",
      "00:16:14 05 | 20 | -0.0093 | -0.0154\n",
      "00:16:25 06 | 01 | -0.0122 |  0.0058\n",
      "00:16:34 06 | 02 |  0.0152 |  0.0146\n",
      "00:16:44 06 | 03 |  0.0288 |  0.0564\n",
      "00:16:54 06 | 04 |  0.0251 |  0.0376\n",
      "00:17:04 06 | 05 |  0.0187 | -0.0075\n",
      "00:17:13 06 | 06 |  0.0223 |  0.0188\n",
      "00:17:23 06 | 07 |  0.0271 |  0.0022\n",
      "00:17:33 06 | 08 | -0.0062 | -0.0006\n",
      "00:17:42 06 | 09 |  0.0273 |  0.0054\n",
      "00:17:52 06 | 10 |  0.0273 |  0.0056\n",
      "00:18:02 06 | 11 |  0.0180 |  0.0069\n",
      "00:18:11 06 | 12 |  0.0340 |  0.0434\n",
      "00:18:21 06 | 13 |  0.0206 |  0.0211\n",
      "00:18:31 06 | 14 |  0.0217 |  0.0056\n",
      "00:18:41 06 | 15 |  0.0359 |  0.0395\n",
      "00:18:50 06 | 16 |  0.0360 |  0.0445\n",
      "00:18:60 06 | 17 |  0.0481 |  0.0341\n",
      "00:19:10 06 | 18 |  0.0155 |  0.0169\n",
      "00:19:19 06 | 19 |  0.0148 | -0.0004\n",
      "00:19:29 06 | 20 |  0.0199 |  0.0156\n",
      "00:19:39 07 | 01 |  0.0092 | -0.0048\n",
      "00:19:49 07 | 02 |  0.0227 |  0.0389\n",
      "00:19:59 07 | 03 |  0.0236 |  0.0477\n",
      "00:20:08 07 | 04 |  0.0267 |  0.0623\n",
      "00:20:18 07 | 05 |  0.0098 |  0.0308\n",
      "00:20:28 07 | 06 |  0.0159 |  0.0488\n",
      "00:20:37 07 | 07 | -0.0008 |  0.0429\n",
      "00:20:47 07 | 08 | -0.0112 | -0.0112\n",
      "00:20:57 07 | 09 | -0.0119 | -0.0065\n",
      "00:21:06 07 | 10 | -0.0003 |  0.0356\n",
      "00:21:16 07 | 11 |  0.0091 |  0.0199\n",
      "00:21:26 07 | 12 | -0.0165 |  0.0250\n",
      "00:21:36 07 | 13 | -0.0094 |  0.0103\n",
      "00:21:46 07 | 14 |  0.0070 |  0.0209\n",
      "00:21:55 07 | 15 | -0.0086 |  0.0105\n",
      "00:22:05 07 | 16 | -0.0008 |  0.0151\n",
      "00:22:15 07 | 17 |  0.0073 |  0.0103\n",
      "00:22:24 07 | 18 |  0.0127 |  0.0306\n",
      "00:22:34 07 | 19 |  0.0015 | -0.0015\n",
      "00:22:44 07 | 20 | -0.0034 | -0.0007\n",
      "00:22:54 08 | 01 |  0.0139 | -0.0084\n",
      "00:23:04 08 | 02 | -0.0109 |  0.0084\n",
      "00:23:14 08 | 03 | -0.0005 |  0.0334\n",
      "00:23:23 08 | 04 |  0.0146 |  0.0062\n",
      "00:23:33 08 | 05 |  0.0182 | -0.0103\n",
      "00:23:43 08 | 06 |  0.0457 |  0.0347\n",
      "00:23:52 08 | 07 |  0.0065 |  0.0068\n",
      "00:24:02 08 | 08 |  0.0108 | -0.0082\n",
      "00:24:12 08 | 09 | -0.0028 | -0.0061\n",
      "00:24:21 08 | 10 |  0.0091 |  0.0113\n",
      "00:24:31 08 | 11 |  0.0025 |  0.0117\n",
      "00:24:41 08 | 12 |  0.0158 |  0.0181\n",
      "00:24:50 08 | 13 |  0.0088 |  0.0351\n",
      "00:25:00 08 | 14 |  0.0114 |  0.0179\n",
      "00:25:10 08 | 15 |  0.0115 |  0.0257\n",
      "00:25:19 08 | 16 |  0.0065 |  0.0410\n",
      "00:25:29 08 | 17 |  0.0032 | -0.0015\n",
      "00:25:39 08 | 18 |  0.0189 |  0.0397\n",
      "00:25:48 08 | 19 |  0.0066 |  0.0291\n",
      "00:25:58 08 | 20 |  0.0246 |  0.0231\n",
      "00:26:09 09 | 01 |  0.0011 |  0.0075\n",
      "00:26:18 09 | 02 |  0.0096 |  0.0108\n",
      "00:26:28 09 | 03 |  0.0106 |  0.0025\n",
      "00:26:38 09 | 04 |  0.0142 |  0.0167\n",
      "00:26:47 09 | 05 | -0.0042 | -0.0165\n",
      "00:26:57 09 | 06 |  0.0108 |  0.0173\n",
      "00:27:07 09 | 07 | -0.0044 | -0.0104\n",
      "00:27:17 09 | 08 | -0.0035 |  0.0037\n",
      "00:27:26 09 | 09 |  0.0075 | -0.0022\n",
      "00:27:36 09 | 10 | -0.0015 | -0.0111\n",
      "00:27:46 09 | 11 |  0.0042 | -0.0069\n",
      "00:27:56 09 | 12 |  0.0085 |  0.0117\n",
      "00:28:05 09 | 13 |  0.0130 |  0.0146\n",
      "00:28:15 09 | 14 |  0.0212 |  0.0143\n",
      "00:28:25 09 | 15 |  0.0137 |  0.0097\n",
      "00:28:35 09 | 16 |  0.0213 |  0.0355\n",
      "00:28:44 09 | 17 |  0.0131 |  0.0110\n",
      "00:28:54 09 | 18 |  0.0009 | -0.0084\n",
      "00:29:04 09 | 19 |  0.0105 |  0.0086\n",
      "00:29:14 09 | 20 | -0.0003 | -0.0033\n",
      "00:29:24 10 | 01 | -0.0450 | -0.0404\n",
      "00:29:34 10 | 02 | -0.0014 |  0.0041\n",
      "00:29:43 10 | 03 | -0.0328 | -0.0300\n",
      "00:29:53 10 | 04 | -0.0166 | -0.0319\n",
      "00:30:03 10 | 05 | -0.0123 | -0.0013\n",
      "00:30:13 10 | 06 | -0.0075 |  0.0048\n",
      "00:30:22 10 | 07 |  0.0142 |  0.0168\n",
      "00:30:32 10 | 08 | -0.0043 | -0.0069\n",
      "00:30:42 10 | 09 | -0.0077 | -0.0135\n",
      "00:30:52 10 | 10 |  0.0013 |  0.0067\n",
      "00:31:02 10 | 11 | -0.0204 | -0.0109\n",
      "00:31:11 10 | 12 | -0.0133 | -0.0206\n",
      "00:31:21 10 | 13 | -0.0133 | -0.0052\n",
      "00:31:31 10 | 14 |  0.0084 | -0.0156\n",
      "00:31:41 10 | 15 | -0.0028 |  0.0077\n",
      "00:31:50 10 | 16 | -0.0114 | -0.0175\n",
      "00:32:00 10 | 17 | -0.0170 | -0.0159\n",
      "00:32:10 10 | 18 | -0.0056 | -0.0190\n",
      "00:32:20 10 | 19 | -0.0159 | -0.0102\n",
      "00:32:29 10 | 20 | -0.0172 | -0.0091\n",
      "00:32:40 11 | 01 |  0.0154 | -0.0018\n",
      "00:32:50 11 | 02 |  0.0231 |  0.0186\n",
      "00:32:59 11 | 03 |  0.0015 |  0.0011\n",
      "00:33:09 11 | 04 |  0.0044 | -0.0055\n",
      "00:33:19 11 | 05 |  0.0126 |  0.0021\n",
      "00:33:29 11 | 06 |  0.0109 |  0.0054\n",
      "00:33:38 11 | 07 |  0.0045 |  0.0098\n",
      "00:33:48 11 | 08 |  0.0134 |  0.0257\n",
      "00:33:58 11 | 09 |  0.0324 |  0.0228\n",
      "00:34:07 11 | 10 |  0.0132 |  0.0128\n",
      "00:34:17 11 | 11 |  0.0202 |  0.0213\n",
      "00:34:27 11 | 12 |  0.0201 |  0.0144\n",
      "00:34:37 11 | 13 |  0.0152 |  0.0044\n",
      "00:34:46 11 | 14 |  0.0170 |  0.0120\n",
      "00:34:56 11 | 15 |  0.0240 |  0.0246\n",
      "00:35:06 11 | 16 |  0.0185 |  0.0176\n",
      "00:35:16 11 | 17 |  0.0272 |  0.0331\n",
      "00:35:26 11 | 18 |  0.0340 |  0.0394\n",
      "00:35:35 11 | 19 |  0.0199 |  0.0292\n",
      "00:35:45 11 | 20 |  0.0282 |  0.0216\n",
      "00:35:56 12 | 01 |  0.0033 | -0.0052\n",
      "00:36:05 12 | 02 | -0.0111 | -0.0018\n",
      "00:36:15 12 | 03 | -0.0011 |  0.0066\n",
      "00:36:25 12 | 04 |  0.0085 |  0.0088\n",
      "00:36:35 12 | 05 |  0.0044 |  0.0303\n",
      "00:36:44 12 | 06 |  0.0017 |  0.0106\n",
      "00:36:54 12 | 07 |  0.0082 |  0.0130\n",
      "00:37:04 12 | 08 | -0.0014 |  0.0127\n",
      "00:37:13 12 | 09 | -0.0052 |  0.0081\n",
      "00:37:23 12 | 10 | -0.0099 |  0.0134\n",
      "00:37:33 12 | 11 | -0.0187 | -0.0182\n",
      "00:37:43 12 | 12 | -0.0069 | -0.0021\n",
      "00:37:52 12 | 13 | -0.0063 |  0.0036\n",
      "00:38:02 12 | 14 | -0.0201 | -0.0229\n",
      "00:38:12 12 | 15 | -0.0061 | -0.0130\n",
      "00:38:21 12 | 16 | -0.0014 | -0.0109\n",
      "00:38:31 12 | 17 | -0.0025 |  0.0004\n",
      "00:38:41 12 | 18 | -0.0051 |  0.0011\n",
      "00:38:51 12 | 19 | -0.0041 | -0.0147\n",
      "00:39:00 12 | 20 | -0.0007 | -0.0038\n",
      "(32, 16) tanh 0 256\n",
      "00:00:04 01 | 01 | -0.0031 | -0.0104\n",
      "00:00:07 01 | 02 | -0.0120 | -0.0192\n",
      "00:00:10 01 | 03 | -0.0067 |  0.0022\n",
      "00:00:13 01 | 04 |  0.0260 |  0.0379\n",
      "00:00:16 01 | 05 |  0.0074 |  0.0027\n",
      "00:00:18 01 | 06 |  0.0077 |  0.0055\n",
      "00:00:21 01 | 07 |  0.0176 |  0.0178\n",
      "00:00:24 01 | 08 |  0.0202 |  0.0240\n",
      "00:00:27 01 | 09 | -0.0092 |  0.0053\n",
      "00:00:30 01 | 10 |  0.0111 |  0.0131\n",
      "00:00:33 01 | 11 |  0.0053 | -0.0105\n",
      "00:00:36 01 | 12 |  0.0093 |  0.0135\n",
      "00:00:39 01 | 13 | -0.0019 |  0.0114\n",
      "00:00:42 01 | 14 | -0.0048 | -0.0113\n",
      "00:00:45 01 | 15 |  0.0081 |  0.0010\n",
      "00:00:48 01 | 16 |  0.0090 |  0.0102\n",
      "00:00:51 01 | 17 |  0.0136 |  0.0155\n",
      "00:00:54 01 | 18 |  0.0155 |  0.0116\n",
      "00:00:57 01 | 19 |  0.0082 |  0.0150\n",
      "00:00:60 01 | 20 |  0.0142 |  0.0132\n",
      "00:01:03 02 | 01 | -0.0171 | -0.0188\n",
      "00:01:06 02 | 02 | -0.0052 | -0.0094\n",
      "00:01:09 02 | 03 | -0.0086 | -0.0031\n",
      "00:01:12 02 | 04 | -0.0202 | -0.0032\n",
      "00:01:15 02 | 05 | -0.0009 |  0.0191\n",
      "00:01:18 02 | 06 |  0.0096 |  0.0296\n",
      "00:01:21 02 | 07 | -0.0017 | -0.0048\n",
      "00:01:24 02 | 08 |  0.0067 |  0.0026\n",
      "00:01:27 02 | 09 |  0.0107 |  0.0094\n",
      "00:01:30 02 | 10 | -0.0013 |  0.0168\n",
      "00:01:33 02 | 11 |  0.0023 | -0.0018\n",
      "00:01:36 02 | 12 |  0.0114 |  0.0165\n",
      "00:01:39 02 | 13 | -0.0017 |  0.0194\n",
      "00:01:42 02 | 14 | -0.0025 | -0.0348\n",
      "00:01:44 02 | 15 |  0.0067 |  0.0064\n",
      "00:01:47 02 | 16 |  0.0112 |  0.0091\n",
      "00:01:50 02 | 17 |  0.0019 |  0.0015\n",
      "00:01:53 02 | 18 |  0.0015 |  0.0013\n",
      "00:01:56 02 | 19 |  0.0152 |  0.0247\n",
      "00:01:59 02 | 20 |  0.0199 |  0.0343\n",
      "00:02:03 03 | 01 | -0.0099 | -0.0259\n",
      "00:02:06 03 | 02 |  0.0028 | -0.0045\n",
      "00:02:09 03 | 03 | -0.0055 |  0.0008\n",
      "00:02:12 03 | 04 |  0.0243 |  0.0248\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:02:15 03 | 05 |  0.0164 |  0.0140\n",
      "00:02:18 03 | 06 | -0.0172 | -0.0333\n",
      "00:02:21 03 | 07 |  0.0109 |  0.0118\n",
      "00:02:24 03 | 08 |  0.0087 |  0.0238\n",
      "00:02:27 03 | 09 | -0.0230 | -0.0348\n",
      "00:02:30 03 | 10 | -0.0147 | -0.0155\n",
      "00:02:33 03 | 11 | -0.0186 | -0.0352\n",
      "00:02:36 03 | 12 | -0.0047 |  0.0031\n",
      "00:02:39 03 | 13 | -0.0004 | -0.0019\n",
      "00:02:42 03 | 14 | -0.0080 | -0.0256\n",
      "00:02:45 03 | 15 |  0.0156 |  0.0128\n",
      "00:02:48 03 | 16 |  0.0186 |  0.0162\n",
      "00:02:50 03 | 17 |  0.0029 | -0.0181\n",
      "00:02:53 03 | 18 |  0.0118 | -0.0115\n",
      "00:02:56 03 | 19 |  0.0078 | -0.0048\n",
      "00:02:59 03 | 20 |  0.0024 | -0.0097\n",
      "00:03:03 04 | 01 | -0.0053 | -0.0151\n",
      "00:03:06 04 | 02 |  0.0392 |  0.0377\n",
      "00:03:09 04 | 03 |  0.0265 |  0.0241\n",
      "00:03:12 04 | 04 |  0.0314 |  0.0310\n",
      "00:03:15 04 | 05 | -0.0360 | -0.0429\n",
      "00:03:18 04 | 06 | -0.0367 | -0.0411\n",
      "00:03:21 04 | 07 |  0.0246 |  0.0049\n",
      "00:03:24 04 | 08 | -0.0296 | -0.0427\n",
      "00:03:27 04 | 09 |  0.0296 |  0.0164\n",
      "00:03:30 04 | 10 | -0.0439 | -0.0467\n",
      "00:03:33 04 | 11 |  0.0238 |  0.0028\n",
      "00:03:36 04 | 12 | -0.0451 | -0.0581\n",
      "00:03:39 04 | 13 | -0.0346 | -0.0367\n",
      "00:03:42 04 | 14 |  0.0242 |  0.0218\n",
      "00:03:45 04 | 15 | -0.0355 | -0.0484\n",
      "00:03:48 04 | 16 | -0.0062 | -0.0167\n",
      "00:03:50 04 | 17 | -0.0350 | -0.0413\n",
      "00:03:53 04 | 18 | -0.0206 | -0.0316\n",
      "00:03:56 04 | 19 |  0.0268 |  0.0105\n",
      "00:03:59 04 | 20 | -0.0389 | -0.0486\n",
      "00:04:03 05 | 01 | -0.0008 | -0.0058\n",
      "00:04:06 05 | 02 |  0.0157 |  0.0141\n",
      "00:04:09 05 | 03 |  0.0018 | -0.0042\n",
      "00:04:12 05 | 04 | -0.0148 | -0.0289\n",
      "00:04:15 05 | 05 |  0.0257 |  0.0288\n",
      "00:04:18 05 | 06 |  0.0050 | -0.0012\n",
      "00:04:21 05 | 07 | -0.0019 | -0.0122\n",
      "00:04:24 05 | 08 | -0.0057 | -0.0140\n",
      "00:04:27 05 | 09 |  0.0058 |  0.0068\n",
      "00:04:30 05 | 10 |  0.0195 | -0.0060\n",
      "00:04:33 05 | 11 | -0.0017 | -0.0165\n",
      "00:04:36 05 | 12 |  0.0161 |  0.0293\n",
      "00:04:39 05 | 13 |  0.0036 | -0.0069\n",
      "00:04:42 05 | 14 | -0.0141 | -0.0295\n",
      "00:04:45 05 | 15 |  0.0018 |  0.0026\n",
      "00:04:48 05 | 16 | -0.0030 | -0.0156\n",
      "00:04:51 05 | 17 |  0.0002 | -0.0011\n",
      "00:04:54 05 | 18 |  0.0106 | -0.0050\n",
      "00:04:57 05 | 19 |  0.0122 |  0.0129\n",
      "00:04:60 05 | 20 | -0.0030 | -0.0184\n",
      "00:05:03 06 | 01 |  0.0106 | -0.0047\n",
      "00:05:06 06 | 02 |  0.0101 | -0.0195\n",
      "00:05:09 06 | 03 |  0.0139 |  0.0181\n",
      "00:05:12 06 | 04 | -0.0035 |  0.0028\n",
      "00:05:15 06 | 05 |  0.0225 |  0.0047\n",
      "00:05:18 06 | 06 |  0.0354 |  0.0267\n",
      "00:05:21 06 | 07 |  0.0332 |  0.0243\n",
      "00:05:24 06 | 08 |  0.0324 |  0.0112\n",
      "00:05:27 06 | 09 |  0.0283 |  0.0258\n",
      "00:05:30 06 | 10 |  0.0252 | -0.0039\n",
      "00:05:33 06 | 11 |  0.0090 | -0.0038\n",
      "00:05:36 06 | 12 |  0.0147 | -0.0154\n",
      "00:05:39 06 | 13 |  0.0068 | -0.0175\n",
      "00:05:42 06 | 14 |  0.0109 |  0.0093\n",
      "00:05:45 06 | 15 |  0.0153 | -0.0005\n",
      "00:05:48 06 | 16 |  0.0059 | -0.0014\n",
      "00:05:51 06 | 17 |  0.0210 |  0.0104\n",
      "00:05:54 06 | 18 |  0.0072 | -0.0017\n",
      "00:05:56 06 | 19 |  0.0116 | -0.0022\n",
      "00:05:59 06 | 20 |  0.0019 |  0.0094\n",
      "00:06:03 07 | 01 |  0.0161 | -0.0013\n",
      "00:06:06 07 | 02 |  0.0313 |  0.0174\n",
      "00:06:09 07 | 03 |  0.0005 |  0.0023\n",
      "00:06:12 07 | 04 |  0.0036 |  0.0110\n",
      "00:06:15 07 | 05 |  0.0131 |  0.0236\n",
      "00:06:18 07 | 06 | -0.0033 |  0.0201\n",
      "00:06:21 07 | 07 |  0.0079 |  0.0186\n",
      "00:06:24 07 | 08 |  0.0121 |  0.0216\n",
      "00:06:27 07 | 09 |  0.0076 |  0.0101\n",
      "00:06:30 07 | 10 | -0.0058 | -0.0104\n",
      "00:06:33 07 | 11 |  0.0106 |  0.0371\n",
      "00:06:36 07 | 12 | -0.0077 |  0.0112\n",
      "00:06:39 07 | 13 |  0.0080 |  0.0283\n",
      "00:06:42 07 | 14 |  0.0022 |  0.0034\n",
      "00:06:44 07 | 15 |  0.0142 |  0.0194\n",
      "00:06:47 07 | 16 |  0.0053 |  0.0056\n",
      "00:06:50 07 | 17 |  0.0065 |  0.0190\n",
      "00:06:53 07 | 18 |  0.0089 |  0.0363\n",
      "00:06:56 07 | 19 | -0.0032 | -0.0206\n",
      "00:06:59 07 | 20 | -0.0047 |  0.0198\n",
      "00:07:03 08 | 01 |  0.0048 |  0.0103\n",
      "00:07:06 08 | 02 |  0.0300 |  0.0235\n",
      "00:07:09 08 | 03 |  0.0107 |  0.0239\n",
      "00:07:12 08 | 04 |  0.0047 |  0.0129\n",
      "00:07:15 08 | 05 |  0.0150 | -0.0039\n",
      "00:07:18 08 | 06 |  0.0338 |  0.0237\n",
      "00:07:20 08 | 07 |  0.0291 |  0.0274\n",
      "00:07:23 08 | 08 |  0.0199 | -0.0033\n",
      "00:07:26 08 | 09 |  0.0305 |  0.0149\n",
      "00:07:29 08 | 10 |  0.0205 |  0.0179\n",
      "00:07:32 08 | 11 |  0.0317 |  0.0154\n",
      "00:07:35 08 | 12 |  0.0385 |  0.0362\n",
      "00:07:38 08 | 13 |  0.0316 |  0.0190\n",
      "00:07:41 08 | 14 |  0.0290 |  0.0259\n",
      "00:07:44 08 | 15 |  0.0388 |  0.0495\n",
      "00:07:47 08 | 16 |  0.0165 |  0.0408\n",
      "00:07:50 08 | 17 |  0.0317 |  0.0163\n",
      "00:07:53 08 | 18 |  0.0178 |  0.0097\n",
      "00:07:56 08 | 19 |  0.0118 |  0.0095\n",
      "00:07:59 08 | 20 |  0.0169 |  0.0148\n",
      "00:08:03 09 | 01 |  0.0158 |  0.0131\n",
      "00:08:06 09 | 02 | -0.0272 | -0.0319\n",
      "00:08:08 09 | 03 |  0.0164 |  0.0175\n",
      "00:08:11 09 | 04 |  0.0135 |  0.0134\n",
      "00:08:14 09 | 05 |  0.0203 |  0.0391\n",
      "00:08:17 09 | 06 |  0.0207 | -0.0064\n",
      "00:08:20 09 | 07 |  0.0121 |  0.0100\n",
      "00:08:23 09 | 08 |  0.0124 | -0.0019\n",
      "00:08:26 09 | 09 |  0.0152 |  0.0156\n",
      "00:08:29 09 | 10 |  0.0098 |  0.0035\n",
      "00:08:32 09 | 11 |  0.0102 | -0.0045\n",
      "00:08:35 09 | 12 | -0.0026 |  0.0029\n",
      "00:08:38 09 | 13 |  0.0066 | -0.0009\n",
      "00:08:41 09 | 14 |  0.0065 |  0.0023\n",
      "00:08:44 09 | 15 |  0.0059 |  0.0193\n",
      "00:08:47 09 | 16 |  0.0025 |  0.0071\n",
      "00:08:50 09 | 17 | -0.0034 | -0.0051\n",
      "00:08:53 09 | 18 |  0.0147 |  0.0060\n",
      "00:08:56 09 | 19 |  0.0084 |  0.0112\n",
      "00:08:59 09 | 20 |  0.0174 |  0.0123\n",
      "00:09:02 10 | 01 | -0.0083 |  0.0005\n",
      "00:09:05 10 | 02 | -0.0198 | -0.0245\n",
      "00:09:08 10 | 03 | -0.0183 | -0.0042\n",
      "00:09:11 10 | 04 | -0.0152 | -0.0164\n",
      "00:09:14 10 | 05 | -0.0064 | -0.0048\n",
      "00:09:17 10 | 06 | -0.0174 |  0.0003\n",
      "00:09:20 10 | 07 | -0.0118 | -0.0142\n",
      "00:09:23 10 | 08 | -0.0142 |  0.0167\n",
      "00:09:26 10 | 09 |  0.0150 |  0.0110\n",
      "00:09:29 10 | 10 | -0.0068 |  0.0028\n",
      "00:09:32 10 | 11 | -0.0178 | -0.0246\n",
      "00:09:35 10 | 12 | -0.0362 | -0.0490\n",
      "00:09:38 10 | 13 | -0.0189 | -0.0184\n",
      "00:09:41 10 | 14 | -0.0144 |  0.0010\n",
      "00:09:44 10 | 15 | -0.0336 | -0.0374\n",
      "00:09:47 10 | 16 | -0.0271 | -0.0317\n",
      "00:09:50 10 | 17 | -0.0281 | -0.0343\n",
      "00:09:53 10 | 18 | -0.0348 | -0.0319\n",
      "00:09:56 10 | 19 | -0.0107 | -0.0256\n",
      "00:09:59 10 | 20 | -0.0276 | -0.0404\n",
      "00:10:02 11 | 01 | -0.0074 | -0.0014\n",
      "00:10:05 11 | 02 |  0.0316 |  0.0421\n",
      "00:10:08 11 | 03 |  0.0269 |  0.0298\n",
      "00:10:11 11 | 04 |  0.0314 |  0.0247\n",
      "00:10:14 11 | 05 | -0.0138 | -0.0084\n",
      "00:10:17 11 | 06 | -0.0138 | -0.0065\n",
      "00:10:20 11 | 07 |  0.0179 |  0.0090\n",
      "00:10:23 11 | 08 |  0.0319 |  0.0381\n",
      "00:10:26 11 | 09 |  0.0036 |  0.0061\n",
      "00:10:29 11 | 10 |  0.0212 |  0.0249\n",
      "00:10:32 11 | 11 |  0.0312 |  0.0409\n",
      "00:10:35 11 | 12 |  0.0024 | -0.0006\n",
      "00:10:38 11 | 13 |  0.0321 |  0.0392\n",
      "00:10:41 11 | 14 |  0.0353 |  0.0400\n",
      "00:10:44 11 | 15 | -0.0061 | -0.0055\n",
      "00:10:47 11 | 16 |  0.0165 |  0.0198\n",
      "00:10:50 11 | 17 |  0.0316 |  0.0218\n",
      "00:10:53 11 | 18 |  0.0265 |  0.0316\n",
      "00:10:56 11 | 19 |  0.0114 |  0.0174\n",
      "00:10:59 11 | 20 |  0.0029 |  0.0039\n",
      "00:11:02 12 | 01 | -0.0037 | -0.0166\n",
      "00:11:05 12 | 02 |  0.0027 | -0.0169\n",
      "00:11:08 12 | 03 |  0.0184 |  0.0121\n",
      "00:11:11 12 | 04 |  0.0191 |  0.0204\n",
      "00:11:14 12 | 05 |  0.0126 | -0.0059\n",
      "00:11:17 12 | 06 |  0.0006 |  0.0314\n",
      "00:11:20 12 | 07 |  0.0080 |  0.0122\n",
      "00:11:23 12 | 08 |  0.0061 |  0.0096\n",
      "00:11:26 12 | 09 |  0.0157 |  0.0284\n",
      "00:11:29 12 | 10 |  0.0094 |  0.0056\n",
      "00:11:32 12 | 11 |  0.0032 |  0.0094\n",
      "00:11:35 12 | 12 |  0.0035 |  0.0003\n",
      "00:11:38 12 | 13 |  0.0056 |  0.0086\n",
      "00:11:41 12 | 14 |  0.0023 | -0.0060\n",
      "00:11:44 12 | 15 |  0.0198 |  0.0075\n",
      "00:11:47 12 | 16 |  0.0132 |  0.0105\n",
      "00:11:50 12 | 17 |  0.0203 |  0.0040\n",
      "00:11:52 12 | 18 |  0.0134 |  0.0060\n",
      "00:11:55 12 | 19 |  0.0067 | -0.0123\n",
      "00:11:58 12 | 20 |  0.0165 |  0.0059\n",
      "(32, 32) tanh 0 64\n",
      "00:00:11 01 | 01 |  0.0182 |  0.0143\n",
      "00:00:20 01 | 02 |  0.0127 |  0.0145\n",
      "00:00:30 01 | 03 |  0.0144 |  0.0162\n",
      "00:00:40 01 | 04 |  0.0090 |  0.0159\n",
      "00:00:50 01 | 05 |  0.0095 | -0.0097\n",
      "00:00:59 01 | 06 |  0.0268 |  0.0354\n",
      "00:01:09 01 | 07 |  0.0190 |  0.0164\n",
      "00:01:19 01 | 08 |  0.0276 |  0.0219\n",
      "00:01:29 01 | 09 |  0.0241 |  0.0301\n",
      "00:01:38 01 | 10 |  0.0034 | -0.0085\n",
      "00:01:48 01 | 11 |  0.0148 |  0.0168\n",
      "00:01:58 01 | 12 |  0.0070 |  0.0146\n",
      "00:02:08 01 | 13 |  0.0062 | -0.0085\n",
      "00:02:18 01 | 14 |  0.0115 |  0.0110\n",
      "00:02:27 01 | 15 |  0.0127 | -0.0112\n",
      "00:02:37 01 | 16 |  0.0144 | -0.0066\n",
      "00:02:47 01 | 17 |  0.0147 |  0.0122\n",
      "00:02:57 01 | 18 | -0.0098 |  0.0034\n",
      "00:03:06 01 | 19 |  0.0073 |  0.0125\n",
      "00:03:16 01 | 20 | -0.0047 | -0.0139\n",
      "00:03:27 02 | 01 |  0.0027 | -0.0143\n",
      "00:03:36 02 | 02 |  0.0083 |  0.0077\n",
      "00:03:46 02 | 03 |  0.0109 |  0.0194\n",
      "00:03:56 02 | 04 |  0.0153 |  0.0294\n",
      "00:04:06 02 | 05 |  0.0120 |  0.0132\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:04:15 02 | 06 |  0.0143 |  0.0186\n",
      "00:04:25 02 | 07 |  0.0197 |  0.0367\n",
      "00:04:35 02 | 08 |  0.0140 |  0.0085\n",
      "00:04:45 02 | 09 |  0.0002 |  0.0061\n",
      "00:04:55 02 | 10 |  0.0054 |  0.0013\n",
      "00:05:04 02 | 11 | -0.0038 | -0.0048\n",
      "00:05:14 02 | 12 | -0.0020 | -0.0122\n",
      "00:05:24 02 | 13 |  0.0147 |  0.0166\n",
      "00:05:34 02 | 14 |  0.0104 |  0.0166\n",
      "00:05:43 02 | 15 | -0.0010 |  0.0013\n",
      "00:05:53 02 | 16 |  0.0123 |  0.0080\n",
      "00:06:03 02 | 17 | -0.0009 |  0.0079\n",
      "00:06:13 02 | 18 | -0.0084 |  0.0142\n",
      "00:06:23 02 | 19 | -0.0105 | -0.0090\n",
      "00:06:32 02 | 20 |  0.0053 |  0.0052\n",
      "00:06:43 03 | 01 | -0.0174 | -0.0206\n",
      "00:06:53 03 | 02 | -0.0211 | -0.0191\n",
      "00:07:02 03 | 03 |  0.0095 | -0.0115\n",
      "00:07:12 03 | 04 |  0.0147 |  0.0184\n",
      "00:07:22 03 | 05 | -0.0117 |  0.0016\n",
      "00:07:31 03 | 06 |  0.0122 |  0.0132\n",
      "00:07:41 03 | 07 | -0.0163 | -0.0243\n",
      "00:07:51 03 | 08 | -0.0193 | -0.0348\n",
      "00:08:01 03 | 09 |  0.0249 |  0.0283\n",
      "00:08:10 03 | 10 | -0.0072 | -0.0136\n",
      "00:08:20 03 | 11 |  0.0192 |  0.0429\n",
      "00:08:30 03 | 12 | -0.0069 | -0.0018\n",
      "00:08:40 03 | 13 |  0.0085 | -0.0024\n",
      "00:08:49 03 | 14 | -0.0178 | -0.0079\n",
      "00:08:59 03 | 15 |  0.0075 | -0.0020\n",
      "00:09:09 03 | 16 | -0.0022 | -0.0222\n",
      "00:09:19 03 | 17 | -0.0273 | -0.0493\n",
      "00:09:28 03 | 18 | -0.0228 | -0.0360\n",
      "00:09:38 03 | 19 |  0.0167 |  0.0257\n",
      "00:09:48 03 | 20 |  0.0122 |  0.0237\n",
      "00:09:58 04 | 01 |  0.0138 |  0.0087\n",
      "00:10:08 04 | 02 |  0.0569 |  0.0510\n",
      "00:10:18 04 | 03 |  0.0191 |  0.0130\n",
      "00:10:28 04 | 04 | -0.0089 |  0.0045\n",
      "00:10:38 04 | 05 |  0.0552 |  0.0546\n",
      "00:10:47 04 | 06 |  0.0165 |  0.0016\n",
      "00:10:57 04 | 07 |  0.0051 |  0.0063\n",
      "00:11:07 04 | 08 | -0.0156 | -0.0241\n",
      "00:11:17 04 | 09 | -0.0144 | -0.0091\n",
      "00:11:26 04 | 10 |  0.0134 |  0.0131\n",
      "00:11:36 04 | 11 |  0.0233 |  0.0296\n",
      "00:11:46 04 | 12 | -0.0041 | -0.0157\n",
      "00:11:56 04 | 13 | -0.0087 | -0.0099\n",
      "00:12:05 04 | 14 |  0.0223 |  0.0291\n",
      "00:12:15 04 | 15 | -0.0063 | -0.0115\n",
      "00:12:25 04 | 16 | -0.0048 | -0.0113\n",
      "00:12:35 04 | 17 |  0.0121 |  0.0112\n",
      "00:12:44 04 | 18 |  0.0089 |  0.0101\n",
      "00:12:54 04 | 19 |  0.0028 |  0.0037\n",
      "00:13:04 04 | 20 | -0.0141 | -0.0003\n",
      "00:13:14 05 | 01 | -0.0094 | -0.0114\n",
      "00:13:24 05 | 02 |  0.0029 |  0.0142\n",
      "00:13:34 05 | 03 |  0.0306 |  0.0284\n",
      "00:13:44 05 | 04 |  0.0251 |  0.0119\n",
      "00:13:53 05 | 05 |  0.0344 |  0.0254\n",
      "00:14:03 05 | 06 |  0.0044 |  0.0061\n",
      "00:14:13 05 | 07 |  0.0187 |  0.0008\n",
      "00:14:22 05 | 08 |  0.0142 |  0.0071\n",
      "00:14:32 05 | 09 | -0.0019 | -0.0039\n",
      "00:14:42 05 | 10 | -0.0031 | -0.0001\n",
      "00:14:52 05 | 11 |  0.0070 |  0.0159\n",
      "00:15:02 05 | 12 |  0.0060 |  0.0201\n",
      "00:15:11 05 | 13 |  0.0138 |  0.0205\n",
      "00:15:21 05 | 14 |  0.0167 |  0.0327\n",
      "00:15:31 05 | 15 |  0.0119 |  0.0123\n",
      "00:15:41 05 | 16 |  0.0235 |  0.0217\n",
      "00:15:50 05 | 17 |  0.0156 |  0.0177\n",
      "00:16:00 05 | 18 | -0.0210 | -0.0203\n",
      "00:16:10 05 | 19 |  0.0186 |  0.0159\n",
      "00:16:20 05 | 20 |  0.0299 |  0.0120\n",
      "00:16:30 06 | 01 |  0.0127 | -0.0041\n",
      "00:16:40 06 | 02 |  0.0076 |  0.0045\n",
      "00:16:50 06 | 03 |  0.0227 |  0.0473\n",
      "00:16:59 06 | 04 |  0.0031 | -0.0027\n",
      "00:17:09 06 | 05 |  0.0005 |  0.0024\n",
      "00:17:19 06 | 06 |  0.0176 |  0.0076\n",
      "00:17:29 06 | 07 |  0.0159 |  0.0034\n",
      "00:17:39 06 | 08 |  0.0244 |  0.0255\n",
      "00:17:49 06 | 09 |  0.0225 |  0.0323\n",
      "00:17:58 06 | 10 |  0.0059 |  0.0126\n",
      "00:18:08 06 | 11 |  0.0203 |  0.0154\n",
      "00:18:18 06 | 12 |  0.0284 |  0.0298\n",
      "00:18:28 06 | 13 |  0.0142 |  0.0081\n",
      "00:18:38 06 | 14 |  0.0308 |  0.0269\n",
      "00:18:47 06 | 15 |  0.0042 |  0.0236\n",
      "00:18:57 06 | 16 |  0.0181 |  0.0177\n",
      "00:19:07 06 | 17 |  0.0217 |  0.0230\n",
      "00:19:17 06 | 18 |  0.0253 |  0.0247\n",
      "00:19:27 06 | 19 |  0.0185 |  0.0120\n",
      "00:19:36 06 | 20 |  0.0200 |  0.0170\n",
      "00:19:47 07 | 01 | -0.0275 | -0.0254\n",
      "00:19:57 07 | 02 |  0.0204 |  0.0438\n",
      "00:20:07 07 | 03 |  0.0115 |  0.0173\n",
      "00:20:16 07 | 04 |  0.0224 |  0.0569\n",
      "00:20:26 07 | 05 |  0.0119 |  0.0352\n",
      "00:20:36 07 | 06 |  0.0156 |  0.0439\n",
      "00:20:46 07 | 07 |  0.0061 |  0.0299\n",
      "00:20:55 07 | 08 |  0.0097 |  0.0343\n",
      "00:21:05 07 | 09 |  0.0054 |  0.0429\n",
      "00:21:15 07 | 10 |  0.0001 |  0.0349\n",
      "00:21:25 07 | 11 |  0.0069 |  0.0213\n",
      "00:21:34 07 | 12 |  0.0116 |  0.0302\n",
      "00:21:44 07 | 13 |  0.0056 |  0.0177\n",
      "00:21:54 07 | 14 |  0.0128 |  0.0174\n",
      "00:22:04 07 | 15 |  0.0183 |  0.0464\n",
      "00:22:14 07 | 16 | -0.0078 |  0.0177\n",
      "00:22:23 07 | 17 | -0.0085 |  0.0153\n",
      "00:22:33 07 | 18 |  0.0062 | -0.0005\n",
      "00:22:43 07 | 19 | -0.0034 |  0.0266\n",
      "00:22:53 07 | 20 | -0.0014 |  0.0194\n",
      "00:23:03 08 | 01 | -0.0259 | -0.0044\n",
      "00:23:13 08 | 02 |  0.0154 |  0.0078\n",
      "00:23:23 08 | 03 |  0.0025 |  0.0176\n",
      "00:23:33 08 | 04 |  0.0262 |  0.0219\n",
      "00:23:42 08 | 05 |  0.0150 |  0.0181\n",
      "00:23:52 08 | 06 | -0.0132 | -0.0219\n",
      "00:24:02 08 | 07 |  0.0262 |  0.0255\n",
      "00:24:12 08 | 08 | -0.0049 |  0.0179\n",
      "00:24:21 08 | 09 |  0.0295 |  0.0228\n",
      "00:24:31 08 | 10 |  0.0028 | -0.0158\n",
      "00:24:41 08 | 11 |  0.0096 |  0.0028\n",
      "00:24:51 08 | 12 |  0.0059 |  0.0175\n",
      "00:25:00 08 | 13 |  0.0145 |  0.0208\n",
      "00:25:10 08 | 14 |  0.0062 |  0.0275\n",
      "00:25:20 08 | 15 |  0.0154 |  0.0359\n",
      "00:25:30 08 | 16 |  0.0115 |  0.0056\n",
      "00:25:40 08 | 17 |  0.0092 |  0.0340\n",
      "00:25:49 08 | 18 |  0.0196 |  0.0184\n",
      "00:25:59 08 | 19 |  0.0016 |  0.0263\n",
      "00:26:09 08 | 20 |  0.0083 |  0.0179\n",
      "00:26:19 09 | 01 |  0.0083 |  0.0016\n",
      "00:26:29 09 | 02 |  0.0267 |  0.0243\n",
      "00:26:39 09 | 03 |  0.0101 |  0.0186\n",
      "00:26:49 09 | 04 |  0.0118 | -0.0093\n",
      "00:26:58 09 | 05 |  0.0088 |  0.0068\n",
      "00:27:08 09 | 06 |  0.0101 |  0.0053\n",
      "00:27:18 09 | 07 |  0.0075 | -0.0181\n",
      "00:27:28 09 | 08 |  0.0162 |  0.0068\n",
      "00:27:38 09 | 09 |  0.0118 |  0.0018\n",
      "00:27:47 09 | 10 |  0.0091 | -0.0040\n",
      "00:27:57 09 | 11 |  0.0186 |  0.0164\n",
      "00:28:07 09 | 12 |  0.0194 |  0.0185\n",
      "00:28:17 09 | 13 |  0.0150 |  0.0084\n",
      "00:28:26 09 | 14 |  0.0154 |  0.0083\n",
      "00:28:36 09 | 15 |  0.0227 |  0.0194\n",
      "00:28:46 09 | 16 |  0.0161 |  0.0131\n",
      "00:28:56 09 | 17 |  0.0132 |  0.0207\n",
      "00:29:05 09 | 18 |  0.0033 |  0.0225\n",
      "00:29:15 09 | 19 |  0.0234 |  0.0356\n",
      "00:29:25 09 | 20 |  0.0092 |  0.0076\n",
      "00:29:35 10 | 01 |  0.0057 | -0.0108\n",
      "00:29:45 10 | 02 | -0.0137 | -0.0302\n",
      "00:29:55 10 | 03 | -0.0298 | -0.0406\n",
      "00:30:05 10 | 04 |  0.0152 |  0.0453\n",
      "00:30:14 10 | 05 | -0.0111 | -0.0174\n",
      "00:30:24 10 | 06 |  0.0007 | -0.0043\n",
      "00:30:34 10 | 07 | -0.0194 | -0.0149\n",
      "00:30:44 10 | 08 | -0.0214 | -0.0231\n",
      "00:30:54 10 | 09 | -0.0209 | -0.0299\n",
      "00:31:03 10 | 10 |  0.0073 |  0.0187\n",
      "00:31:13 10 | 11 | -0.0433 | -0.0535\n",
      "00:31:23 10 | 12 | -0.0434 | -0.0636\n",
      "00:31:32 10 | 13 | -0.0036 | -0.0052\n",
      "00:31:42 10 | 14 | -0.0332 | -0.0335\n",
      "00:31:52 10 | 15 | -0.0258 | -0.0319\n",
      "00:32:02 10 | 16 | -0.0298 | -0.0423\n",
      "00:32:11 10 | 17 | -0.0386 | -0.0434\n",
      "00:32:21 10 | 18 | -0.0181 | -0.0256\n",
      "00:32:31 10 | 19 | -0.0314 | -0.0314\n",
      "00:32:41 10 | 20 | -0.0157 | -0.0091\n",
      "00:32:51 11 | 01 |  0.0029 |  0.0000\n",
      "00:33:01 11 | 02 |  0.0005 | -0.0033\n",
      "00:33:10 11 | 03 |  0.0067 |  0.0045\n",
      "00:33:20 11 | 04 |  0.0091 | -0.0065\n",
      "00:33:30 11 | 05 |  0.0156 |  0.0099\n",
      "00:33:40 11 | 06 |  0.0125 |  0.0064\n",
      "00:33:49 11 | 07 |  0.0269 |  0.0256\n",
      "00:33:59 11 | 08 |  0.0131 | -0.0038\n",
      "00:34:09 11 | 09 |  0.0165 |  0.0218\n",
      "00:34:18 11 | 10 |  0.0008 |  0.0046\n",
      "00:34:28 11 | 11 |  0.0017 | -0.0012\n",
      "00:34:38 11 | 12 |  0.0085 |  0.0142\n",
      "00:34:48 11 | 13 |  0.0215 |  0.0126\n",
      "00:34:57 11 | 14 |  0.0209 |  0.0292\n",
      "00:35:07 11 | 15 |  0.0324 |  0.0373\n",
      "00:35:17 11 | 16 |  0.0094 |  0.0126\n",
      "00:35:26 11 | 17 |  0.0082 |  0.0103\n",
      "00:35:36 11 | 18 |  0.0099 |  0.0231\n",
      "00:35:46 11 | 19 |  0.0139 |  0.0174\n",
      "00:35:56 11 | 20 |  0.0319 |  0.0282\n",
      "00:36:06 12 | 01 |  0.0053 | -0.0079\n",
      "00:36:16 12 | 02 |  0.0001 |  0.0087\n",
      "00:36:25 12 | 03 |  0.0079 |  0.0017\n",
      "00:36:35 12 | 04 | -0.0136 | -0.0337\n",
      "00:36:45 12 | 05 |  0.0000 |  0.0027\n",
      "00:36:54 12 | 06 | -0.0062 |  0.0009\n",
      "00:37:04 12 | 07 | -0.0036 | -0.0070\n",
      "00:37:14 12 | 08 |  0.0048 | -0.0071\n",
      "00:37:23 12 | 09 | -0.0007 | -0.0011\n",
      "00:37:33 12 | 10 |  0.0054 |  0.0179\n",
      "00:37:43 12 | 11 | -0.0005 | -0.0012\n",
      "00:37:52 12 | 12 |  0.0229 |  0.0041\n",
      "00:38:02 12 | 13 |  0.0334 |  0.0402\n",
      "00:38:12 12 | 14 |  0.0183 |  0.0092\n",
      "00:38:21 12 | 15 |  0.0315 |  0.0348\n",
      "00:38:31 12 | 16 |  0.0345 |  0.0369\n",
      "00:38:41 12 | 17 |  0.0288 |  0.0239\n",
      "00:38:50 12 | 18 |  0.0338 |  0.0259\n",
      "00:38:60 12 | 19 |  0.0299 |  0.0342\n",
      "00:39:10 12 | 20 |  0.0224 |  0.0338\n",
      "(32, 32) tanh 0 256\n",
      "00:00:04 01 | 01 |  0.0114 |  0.0144\n",
      "00:00:07 01 | 02 | -0.0221 | -0.0232\n",
      "00:00:10 01 | 03 |  0.0097 |  0.0268\n",
      "00:00:13 01 | 04 |  0.0048 |  0.0095\n",
      "00:00:16 01 | 05 |  0.0270 |  0.0273\n",
      "00:00:19 01 | 06 | -0.0016 |  0.0057\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:00:22 01 | 07 |  0.0037 |  0.0078\n",
      "00:00:24 01 | 08 |  0.0003 |  0.0023\n",
      "00:00:27 01 | 09 |  0.0126 |  0.0090\n",
      "00:00:30 01 | 10 |  0.0044 | -0.0005\n",
      "00:00:33 01 | 11 |  0.0229 |  0.0452\n",
      "00:00:36 01 | 12 | -0.0004 | -0.0066\n",
      "00:00:39 01 | 13 | -0.0054 | -0.0149\n",
      "00:00:42 01 | 14 |  0.0095 | -0.0033\n",
      "00:00:45 01 | 15 |  0.0039 |  0.0025\n",
      "00:00:48 01 | 16 | -0.0019 | -0.0069\n",
      "00:00:51 01 | 17 | -0.0009 |  0.0064\n",
      "00:00:54 01 | 18 |  0.0128 |  0.0159\n",
      "00:00:57 01 | 19 |  0.0172 |  0.0195\n",
      "00:01:00 01 | 20 |  0.0104 |  0.0063\n",
      "00:01:04 02 | 01 |  0.0090 |  0.0033\n",
      "00:01:07 02 | 02 |  0.0090 |  0.0050\n",
      "00:01:10 02 | 03 |  0.0099 |  0.0066\n",
      "00:01:13 02 | 04 |  0.0088 |  0.0031\n",
      "00:01:16 02 | 05 |  0.0034 |  0.0042\n",
      "00:01:19 02 | 06 |  0.0137 |  0.0206\n",
      "00:01:21 02 | 07 | -0.0038 |  0.0033\n",
      "00:01:24 02 | 08 |  0.0055 |  0.0195\n",
      "00:01:27 02 | 09 |  0.0160 |  0.0266\n",
      "00:01:30 02 | 10 | -0.0082 | -0.0021\n",
      "00:01:33 02 | 11 |  0.0106 |  0.0124\n",
      "00:01:36 02 | 12 |  0.0018 |  0.0109\n",
      "00:01:39 02 | 13 | -0.0063 |  0.0097\n",
      "00:01:42 02 | 14 |  0.0055 |  0.0085\n",
      "00:01:45 02 | 15 |  0.0110 |  0.0117\n",
      "00:01:48 02 | 16 | -0.0005 |  0.0021\n",
      "00:01:51 02 | 17 | -0.0121 | -0.0083\n",
      "00:01:54 02 | 18 | -0.0081 | -0.0202\n",
      "00:01:57 02 | 19 | -0.0138 | -0.0146\n",
      "00:02:00 02 | 20 | -0.0023 | -0.0070\n",
      "00:02:04 03 | 01 |  0.0011 | -0.0055\n",
      "00:02:07 03 | 02 |  0.0110 |  0.0032\n",
      "00:02:10 03 | 03 | -0.0194 | -0.0377\n",
      "00:02:13 03 | 04 |  0.0377 |  0.0356\n",
      "00:02:16 03 | 05 | -0.0204 | -0.0095\n",
      "00:02:19 03 | 06 | -0.0157 | -0.0268\n",
      "00:02:22 03 | 07 |  0.0294 |  0.0223\n",
      "00:02:25 03 | 08 |  0.0263 |  0.0203\n",
      "00:02:28 03 | 09 |  0.0130 |  0.0008\n",
      "00:02:31 03 | 10 |  0.0067 | -0.0019\n",
      "00:02:33 03 | 11 |  0.0168 |  0.0063\n",
      "00:02:36 03 | 12 |  0.0084 | -0.0036\n",
      "00:02:39 03 | 13 |  0.0184 |  0.0100\n",
      "00:02:42 03 | 14 | -0.0155 | -0.0267\n",
      "00:02:45 03 | 15 |  0.0100 | -0.0006\n",
      "00:02:48 03 | 16 | -0.0126 | -0.0380\n",
      "00:02:51 03 | 17 | -0.0058 | -0.0196\n",
      "00:02:54 03 | 18 |  0.0001 | -0.0171\n",
      "00:02:57 03 | 19 | -0.0157 | -0.0198\n",
      "00:03:00 03 | 20 |  0.0019 | -0.0068\n",
      "00:03:04 04 | 01 | -0.0317 | -0.0172\n",
      "00:03:07 04 | 02 | -0.0297 | -0.0241\n",
      "00:03:10 04 | 03 | -0.0239 | -0.0141\n",
      "00:03:13 04 | 04 |  0.0397 |  0.0268\n",
      "00:03:16 04 | 05 | -0.0135 | -0.0050\n",
      "00:03:19 04 | 06 | -0.0227 | -0.0286\n",
      "00:03:22 04 | 07 |  0.0236 |  0.0232\n",
      "00:03:25 04 | 08 |  0.0300 |  0.0283\n",
      "00:03:28 04 | 09 | -0.0007 | -0.0044\n",
      "00:03:31 04 | 10 | -0.0316 | -0.0464\n",
      "00:03:34 04 | 11 | -0.0095 | -0.0240\n",
      "00:03:37 04 | 12 |  0.0208 |  0.0028\n",
      "00:03:40 04 | 13 |  0.0299 |  0.0316\n",
      "00:03:43 04 | 14 |  0.0286 |  0.0114\n",
      "00:03:46 04 | 15 |  0.0037 | -0.0171\n",
      "00:03:48 04 | 16 | -0.0103 | -0.0372\n",
      "00:03:51 04 | 17 | -0.0209 | -0.0233\n",
      "00:03:54 04 | 18 |  0.0103 |  0.0108\n",
      "00:03:57 04 | 19 | -0.0314 | -0.0545\n",
      "00:04:00 04 | 20 |  0.0228 |  0.0191\n",
      "00:04:04 05 | 01 | -0.0018 | -0.0059\n",
      "00:04:07 05 | 02 |  0.0034 |  0.0062\n",
      "00:04:10 05 | 03 |  0.0011 | -0.0005\n",
      "00:04:13 05 | 04 |  0.0348 |  0.0142\n",
      "00:04:16 05 | 05 |  0.0238 | -0.0012\n",
      "00:04:19 05 | 06 |  0.0197 |  0.0085\n",
      "00:04:22 05 | 07 |  0.0049 | -0.0057\n",
      "00:04:25 05 | 08 |  0.0194 |  0.0254\n",
      "00:04:28 05 | 09 |  0.0216 |  0.0215\n",
      "00:04:31 05 | 10 |  0.0005 |  0.0132\n",
      "00:04:34 05 | 11 | -0.0042 | -0.0121\n",
      "00:04:37 05 | 12 |  0.0267 |  0.0218\n",
      "00:04:40 05 | 13 |  0.0107 |  0.0057\n",
      "00:04:43 05 | 14 |  0.0316 |  0.0278\n",
      "00:04:46 05 | 15 | -0.0125 | -0.0032\n",
      "00:04:49 05 | 16 |  0.0150 |  0.0310\n",
      "00:04:52 05 | 17 | -0.0047 |  0.0037\n",
      "00:04:55 05 | 18 | -0.0037 |  0.0051\n",
      "00:04:58 05 | 19 | -0.0056 | -0.0083\n",
      "00:05:01 05 | 20 |  0.0037 | -0.0028\n",
      "00:05:04 06 | 01 |  0.0063 | -0.0048\n",
      "00:05:07 06 | 02 | -0.0209 | -0.0290\n",
      "00:05:10 06 | 03 | -0.0002 | -0.0122\n",
      "00:05:13 06 | 04 |  0.0353 |  0.0220\n",
      "00:05:16 06 | 05 |  0.0217 |  0.0383\n",
      "00:05:19 06 | 06 |  0.0269 |  0.0332\n",
      "00:05:22 06 | 07 |  0.0224 |  0.0307\n",
      "00:05:25 06 | 08 |  0.0319 |  0.0654\n",
      "00:05:28 06 | 09 |  0.0323 |  0.0533\n",
      "00:05:31 06 | 10 |  0.0369 |  0.0272\n",
      "00:05:34 06 | 11 |  0.0364 |  0.0370\n",
      "00:05:37 06 | 12 |  0.0304 |  0.0500\n",
      "00:05:40 06 | 13 |  0.0310 |  0.0438\n",
      "00:05:43 06 | 14 |  0.0217 |  0.0371\n",
      "00:05:46 06 | 15 |  0.0234 |  0.0141\n",
      "00:05:49 06 | 16 |  0.0353 |  0.0494\n",
      "00:05:52 06 | 17 |  0.0343 |  0.0215\n",
      "00:05:55 06 | 18 |  0.0196 |  0.0210\n",
      "00:05:58 06 | 19 |  0.0110 |  0.0082\n",
      "00:06:01 06 | 20 |  0.0328 |  0.0527\n",
      "00:06:04 07 | 01 |  0.0031 |  0.0033\n",
      "00:06:07 07 | 02 |  0.0181 |  0.0232\n",
      "00:06:10 07 | 03 |  0.0014 |  0.0153\n",
      "00:06:13 07 | 04 |  0.0114 |  0.0270\n",
      "00:06:16 07 | 05 |  0.0265 |  0.0474\n",
      "00:06:19 07 | 06 |  0.0418 |  0.0472\n",
      "00:06:22 07 | 07 |  0.0322 |  0.0381\n",
      "00:06:25 07 | 08 |  0.0100 |  0.0308\n",
      "00:06:28 07 | 09 |  0.0212 |  0.0448\n",
      "00:06:31 07 | 10 |  0.0303 |  0.0481\n",
      "00:06:34 07 | 11 |  0.0126 |  0.0092\n",
      "00:06:37 07 | 12 |  0.0192 |  0.0211\n",
      "00:06:40 07 | 13 |  0.0308 |  0.0540\n",
      "00:06:42 07 | 14 |  0.0147 |  0.0319\n",
      "00:06:45 07 | 15 |  0.0095 |  0.0204\n",
      "00:06:48 07 | 16 |  0.0059 |  0.0285\n",
      "00:06:51 07 | 17 |  0.0209 |  0.0352\n",
      "00:06:54 07 | 18 |  0.0248 |  0.0381\n",
      "00:06:57 07 | 19 |  0.0324 |  0.0488\n",
      "00:07:00 07 | 20 |  0.0297 |  0.0571\n",
      "00:07:04 08 | 01 |  0.0116 |  0.0139\n",
      "00:07:07 08 | 02 |  0.0104 | -0.0073\n",
      "00:07:10 08 | 03 | -0.0016 |  0.0294\n",
      "00:07:13 08 | 04 |  0.0163 |  0.0133\n",
      "00:07:16 08 | 05 |  0.0273 |  0.0389\n",
      "00:07:18 08 | 06 |  0.0176 |  0.0162\n",
      "00:07:21 08 | 07 |  0.0058 |  0.0013\n",
      "00:07:24 08 | 08 |  0.0110 |  0.0237\n",
      "00:07:27 08 | 09 | -0.0023 |  0.0115\n",
      "00:07:30 08 | 10 |  0.0142 |  0.0092\n",
      "00:07:33 08 | 11 |  0.0102 |  0.0135\n",
      "00:07:36 08 | 12 |  0.0043 |  0.0135\n",
      "00:07:39 08 | 13 |  0.0022 |  0.0018\n",
      "00:07:42 08 | 14 | -0.0037 | -0.0013\n",
      "00:07:45 08 | 15 |  0.0038 |  0.0072\n",
      "00:07:48 08 | 16 |  0.0222 |  0.0319\n",
      "00:07:51 08 | 17 |  0.0193 |  0.0099\n",
      "00:07:54 08 | 18 |  0.0059 |  0.0069\n",
      "00:07:57 08 | 19 |  0.0224 |  0.0204\n",
      "00:07:60 08 | 20 |  0.0089 | -0.0056\n",
      "00:08:04 09 | 01 | -0.0043 |  0.0101\n",
      "00:08:07 09 | 02 |  0.0227 |  0.0055\n",
      "00:08:10 09 | 03 |  0.0259 |  0.0230\n",
      "00:08:12 09 | 04 |  0.0121 |  0.0057\n",
      "00:08:15 09 | 05 |  0.0070 |  0.0010\n",
      "00:08:18 09 | 06 | -0.0006 | -0.0008\n",
      "00:08:21 09 | 07 |  0.0040 |  0.0096\n",
      "00:08:24 09 | 08 |  0.0092 |  0.0007\n",
      "00:08:27 09 | 09 | -0.0026 | -0.0245\n",
      "00:08:30 09 | 10 | -0.0075 | -0.0183\n",
      "00:08:33 09 | 11 | -0.0032 | -0.0035\n",
      "00:08:36 09 | 12 | -0.0008 | -0.0040\n",
      "00:08:39 09 | 13 |  0.0115 |  0.0046\n",
      "00:08:42 09 | 14 |  0.0010 | -0.0038\n",
      "00:08:45 09 | 15 |  0.0085 |  0.0075\n",
      "00:08:48 09 | 16 | -0.0003 | -0.0166\n",
      "00:08:51 09 | 17 |  0.0034 |  0.0064\n",
      "00:08:54 09 | 18 |  0.0028 |  0.0059\n",
      "00:08:57 09 | 19 | -0.0111 | -0.0038\n",
      "00:08:60 09 | 20 | -0.0090 | -0.0183\n",
      "00:09:04 10 | 01 |  0.0115 |  0.0060\n",
      "00:09:07 10 | 02 | -0.0208 | -0.0391\n",
      "00:09:10 10 | 03 | -0.0083 | -0.0032\n",
      "00:09:12 10 | 04 |  0.0022 | -0.0169\n",
      "00:09:15 10 | 05 |  0.0004 |  0.0019\n",
      "00:09:18 10 | 06 | -0.0158 | -0.0293\n",
      "00:09:21 10 | 07 | -0.0208 | -0.0288\n",
      "00:09:24 10 | 08 |  0.0106 |  0.0030\n",
      "00:09:27 10 | 09 | -0.0068 | -0.0042\n",
      "00:09:30 10 | 10 |  0.0076 |  0.0093\n",
      "00:09:33 10 | 11 |  0.0031 |  0.0147\n",
      "00:09:36 10 | 12 | -0.0241 | -0.0129\n",
      "00:09:39 10 | 13 | -0.0368 | -0.0377\n",
      "00:09:42 10 | 14 | -0.0417 | -0.0524\n",
      "00:09:45 10 | 15 | -0.0382 | -0.0423\n",
      "00:09:48 10 | 16 | -0.0525 | -0.0670\n",
      "00:09:51 10 | 17 | -0.0598 | -0.0773\n",
      "00:09:54 10 | 18 | -0.0373 | -0.0588\n",
      "00:09:57 10 | 19 | -0.0356 | -0.0413\n",
      "00:09:60 10 | 20 | -0.0435 | -0.0556\n",
      "00:10:03 11 | 01 | -0.0045 |  0.0004\n",
      "00:10:06 11 | 02 |  0.0035 |  0.0051\n",
      "00:10:09 11 | 03 |  0.0305 |  0.0396\n",
      "00:10:12 11 | 04 |  0.0191 |  0.0178\n",
      "00:10:15 11 | 05 |  0.0132 |  0.0170\n",
      "00:10:18 11 | 06 |  0.0146 |  0.0119\n",
      "00:10:21 11 | 07 |  0.0146 |  0.0144\n",
      "00:10:24 11 | 08 |  0.0319 |  0.0306\n",
      "00:10:27 11 | 09 |  0.0259 |  0.0231\n",
      "00:10:30 11 | 10 |  0.0123 |  0.0013\n",
      "00:10:33 11 | 11 |  0.0069 |  0.0020\n",
      "00:10:36 11 | 12 |  0.0024 | -0.0198\n",
      "00:10:39 11 | 13 |  0.0023 |  0.0034\n",
      "00:10:42 11 | 14 |  0.0152 |  0.0116\n",
      "00:10:45 11 | 15 |  0.0058 |  0.0003\n",
      "00:10:48 11 | 16 |  0.0130 |  0.0028\n",
      "00:10:51 11 | 17 |  0.0232 |  0.0220\n",
      "00:10:54 11 | 18 |  0.0184 |  0.0020\n",
      "00:10:57 11 | 19 |  0.0233 |  0.0182\n",
      "00:10:60 11 | 20 |  0.0142 |  0.0106\n",
      "00:11:03 12 | 01 | -0.0012 | -0.0014\n",
      "00:11:06 12 | 02 |  0.0055 | -0.0093\n",
      "00:11:09 12 | 03 |  0.0055 |  0.0015\n",
      "00:11:12 12 | 04 | -0.0122 | -0.0242\n",
      "00:11:15 12 | 05 |  0.0019 | -0.0094\n",
      "00:11:18 12 | 06 | -0.0009 |  0.0014\n",
      "00:11:21 12 | 07 |  0.0052 |  0.0108\n",
      "00:11:24 12 | 08 |  0.0070 |  0.0064\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00:11:27 12 | 09 | -0.0050 | -0.0114\n",
      "00:11:30 12 | 10 | -0.0067 | -0.0144\n",
      "00:11:33 12 | 11 |  0.0083 |  0.0136\n",
      "00:11:36 12 | 12 |  0.0047 |  0.0099\n",
      "00:11:39 12 | 13 |  0.0070 |  0.0210\n",
      "00:11:42 12 | 14 |  0.0164 |  0.0093\n",
      "00:11:45 12 | 15 |  0.0154 |  0.0333\n",
      "00:11:48 12 | 16 |  0.0296 |  0.0326\n",
      "00:11:51 12 | 17 |  0.0363 |  0.0441\n",
      "00:11:54 12 | 18 |  0.0252 |  0.0348\n",
      "00:11:57 12 | 19 |  0.0371 |  0.0307\n",
      "00:11:59 12 | 20 |  0.0305 |  0.0389\n"
     ]
    }
   ],
   "source": [
    "ic = []\n",
    "scaler = StandardScaler()\n",
    "for params in param_grid:\n",
    "    dense_layers, activation, dropout = params\n",
    "    for batch_size in [64, 256]:\n",
    "        print(dense_layers, activation, dropout, batch_size)\n",
    "        checkpoint_dir = checkpoint_path / str(dense_layers) / activation / str(dropout) / str(batch_size)\n",
    "        if not checkpoint_dir.exists():\n",
    "            checkpoint_dir.mkdir(parents=True, exist_ok=True)\n",
    "        start = time()\n",
    "        for fold, (train_idx, test_idx) in enumerate(cv.split(X_cv)):\n",
    "            x_train, y_train, x_val, y_val = get_train_valid_data(X_cv, y_cv, train_idx, test_idx)\n",
    "            x_train = scaler.fit_transform(x_train)\n",
    "            x_val = scaler.transform(x_val)\n",
    "            preds = y_val.to_frame('actual')\n",
    "            r = pd.DataFrame(index=y_val.groupby(level='date').size().index)\n",
    "            model = make_model(dense_layers, activation, dropout)\n",
    "            for epoch in range(20):            \n",
    "                model.fit(x_train,\n",
    "                          y_train,\n",
    "                          batch_size=batch_size,\n",
    "                          epochs=1,\n",
    "                          verbose=0,\n",
    "                          shuffle=True,\n",
    "                          validation_data=(x_val, y_val))\n",
    "                model.save_weights((checkpoint_path / f'ckpt_{fold}_{epoch}').as_posix())\n",
    "                preds[epoch] = model.predict(x_val).squeeze()\n",
    "                r[epoch] = preds.groupby(level='date').apply(lambda x: spearmanr(x.actual, x[epoch])[0]).to_frame(epoch)\n",
    "                print(format_time(time()-start), f'{fold + 1:02d} | {epoch + 1:02d} | {r[epoch].mean():7.4f} | {r[epoch].median():7.4f}')\n",
    "            ic.append(r.assign(dense_layers=str(dense_layers), \n",
    "                               activation=activation, \n",
    "                               dropout=dropout,\n",
    "                               batch_size=batch_size,\n",
    "                               fold=fold))       \n",
    "\n",
    "        t = time()-start\n",
    "        pd.concat(ic).to_hdf(results_path / 'scores.h5', 'ic_by_day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate predictive performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T09:03:16.935076Z",
     "start_time": "2020-06-22T09:03:16.933294Z"
    }
   },
   "outputs": [],
   "source": [
    "params = ['dense_layers', 'dropout', 'batch_size']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T09:03:16.973444Z",
     "start_time": "2020-06-22T09:03:16.935966Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 18144 entries, 2017-08-30 to 2015-03-02\n",
      "Data columns (total 24 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   0             18144 non-null  float64\n",
      " 1   1             18144 non-null  float64\n",
      " 2   2             18144 non-null  float64\n",
      " 3   3             18144 non-null  float64\n",
      " 4   4             18144 non-null  float64\n",
      " 5   5             18144 non-null  float64\n",
      " 6   6             18144 non-null  float64\n",
      " 7   7             18144 non-null  float64\n",
      " 8   8             18144 non-null  float64\n",
      " 9   9             18144 non-null  float64\n",
      " 10  10            18144 non-null  float64\n",
      " 11  11            18144 non-null  float64\n",
      " 12  12            18144 non-null  float64\n",
      " 13  13            18144 non-null  float64\n",
      " 14  14            18144 non-null  float64\n",
      " 15  15            18144 non-null  float64\n",
      " 16  16            18144 non-null  float64\n",
      " 17  17            18144 non-null  float64\n",
      " 18  18            18144 non-null  float64\n",
      " 19  19            18144 non-null  float64\n",
      " 20  dense_layers  18144 non-null  object \n",
      " 21  dropout       18144 non-null  float64\n",
      " 22  batch_size    18144 non-null  int64  \n",
      " 23  fold          18144 non-null  int64  \n",
      "dtypes: float64(21), int64(2), object(1)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "ic = pd.read_hdf(results_path / 'scores.h5', 'ic_by_day').drop('activation', axis=1)\n",
    "ic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T09:03:16.981070Z",
     "start_time": "2020-06-22T09:03:16.975222Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dense_layers  dropout  batch_size\n",
       "(16, 8)       0.0      64            756\n",
       "                       256           756\n",
       "              0.1      64            756\n",
       "                       256           756\n",
       "              0.2      64            756\n",
       "                       256           756\n",
       "(32, 16)      0.0      64            756\n",
       "                       256           756\n",
       "              0.1      64            756\n",
       "                       256           756\n",
       "              0.2      64            756\n",
       "                       256           756\n",
       "(32, 32)      0.0      64            756\n",
       "                       256           756\n",
       "              0.1      64            756\n",
       "                       256           756\n",
       "              0.2      64            756\n",
       "                       256           756\n",
       "(64, 32)      0.0      64            756\n",
       "                       256           756\n",
       "              0.1      64            756\n",
       "                       256           756\n",
       "              0.2      64            756\n",
       "                       256           756\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ic.groupby(params).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T09:03:17.024711Z",
     "start_time": "2020-06-22T09:03:16.982017Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 362880 entries, 0 to 362879\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   dense_layers  362880 non-null  object \n",
      " 1   dropout       362880 non-null  float64\n",
      " 2   batch_size    362880 non-null  int64  \n",
      " 3   fold          362880 non-null  int64  \n",
      " 4   epoch         362880 non-null  object \n",
      " 5   ic            362880 non-null  float64\n",
      "dtypes: float64(2), int64(2), object(2)\n",
      "memory usage: 16.6+ MB\n"
     ]
    }
   ],
   "source": [
    "ic_long = pd.melt(ic, id_vars=params + ['fold'], var_name='epoch', value_name='ic')\n",
    "ic_long.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T09:03:17.066133Z",
     "start_time": "2020-06-22T09:03:17.025686Z"
    }
   },
   "outputs": [],
   "source": [
    "ic_long = ic_long.groupby(params+ ['epoch', 'fold']).ic.mean().to_frame('ic').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T10:52:03.780459Z",
     "start_time": "2020-06-22T10:51:57.820200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.relplot(x='epoch', y='ic', col='dense_layers', row='dropout', \n",
    "                data=ic_long[ic_long.dropout>0], kind='line')\n",
    "g.map(plt.axhline, y=0, ls='--', c='k', lw=1)\n",
    "g.savefig(results_path / 'ic_lineplot', dpi=300);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T10:52:06.995426Z",
     "start_time": "2020-06-22T10:52:06.979836Z"
    }
   },
   "outputs": [],
   "source": [
    "def run_ols(ic):\n",
    "    ic.dense_layers = ic.dense_layers.str.replace(', ', '-').str.replace('(', '').str.replace(')', '')\n",
    "    data = pd.melt(ic, id_vars=params, var_name='epoch', value_name='ic')\n",
    "    data.epoch = data.epoch.astype(int).astype(str).apply(lambda x: f'{int(x):02.0f}')\n",
    "    model_data = pd.get_dummies(data.sort_values(params + ['epoch']), columns=['epoch'] + params, drop_first=True).sort_index(1)\n",
    "    model_data.columns = [s.split('_')[-1] for s in model_data.columns]\n",
    "    model = sm.OLS(endog=model_data.ic, exog=sm.add_constant(model_data.drop('ic', axis=1)))\n",
    "    return model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T10:52:08.144051Z",
     "start_time": "2020-06-22T10:52:07.304541Z"
    }
   },
   "outputs": [],
   "source": [
    "model = run_ols(ic.drop('fold', axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T10:52:08.513752Z",
     "start_time": "2020-06-22T10:52:08.433474Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                     ic   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                  0.000\n",
      "Method:                 Least Squares   F-statistic:                     3.108\n",
      "Date:                Mon, 22 Jun 2020   Prob (F-statistic):           2.60e-07\n",
      "Time:                        06:52:08   Log-Likelihood:             2.3148e+05\n",
      "No. Observations:              362880   AIC:                        -4.629e+05\n",
      "Df Residuals:                  362854   BIC:                        -4.626e+05\n",
      "Df Model:                          25                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0021      0.001      1.960      0.050    3.23e-07       0.004\n",
      "256            0.0010      0.000      2.268      0.023       0.000       0.002\n",
      "32-16         -0.0014      0.001     -2.404      0.016      -0.003      -0.000\n",
      "32-32          0.0009      0.001      1.523      0.128      -0.000       0.002\n",
      "64-32         -0.0006      0.001     -1.020      0.308      -0.002       0.001\n",
      "0.1            0.0015      0.001      2.835      0.005       0.000       0.002\n",
      "0.2            0.0013      0.001      2.532      0.011       0.000       0.002\n",
      "01             0.0030      0.001      2.256      0.024       0.000       0.006\n",
      "02             0.0047      0.001      3.466      0.001       0.002       0.007\n",
      "03             0.0050      0.001      3.697      0.000       0.002       0.008\n",
      "04             0.0055      0.001      4.111      0.000       0.003       0.008\n",
      "05             0.0052      0.001      3.876      0.000       0.003       0.008\n",
      "06             0.0062      0.001      4.645      0.000       0.004       0.009\n",
      "07             0.0048      0.001      3.577      0.000       0.002       0.007\n",
      "08             0.0050      0.001      3.705      0.000       0.002       0.008\n",
      "09             0.0043      0.001      3.191      0.001       0.002       0.007\n",
      "10             0.0041      0.001      3.066      0.002       0.001       0.007\n",
      "11             0.0032      0.001      2.404      0.016       0.001       0.006\n",
      "12             0.0034      0.001      2.543      0.011       0.001       0.006\n",
      "13             0.0035      0.001      2.591      0.010       0.001       0.006\n",
      "14             0.0035      0.001      2.613      0.009       0.001       0.006\n",
      "15             0.0036      0.001      2.665      0.008       0.001       0.006\n",
      "16             0.0023      0.001      1.746      0.081      -0.000       0.005\n",
      "17             0.0016      0.001      1.195      0.232      -0.001       0.004\n",
      "18             0.0022      0.001      1.615      0.106      -0.000       0.005\n",
      "19             0.0028      0.001      2.074      0.038       0.000       0.005\n",
      "==============================================================================\n",
      "Omnibus:                     5739.882   Durbin-Watson:                   1.981\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            11403.525\n",
      "Skew:                           0.010   Prob(JB):                         0.00\n",
      "Kurtosis:                       3.868   Cond. No.                         27.2\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T10:52:13.437203Z",
     "start_time": "2020-06-22T10:52:12.851617Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 4))\n",
    "\n",
    "ci = model.conf_int()\n",
    "errors = ci[1].sub(ci[0]).div(2)\n",
    "\n",
    "coefs = (model.params.to_frame('coef').assign(error=errors)\n",
    "         .reset_index().rename(columns={'index': 'variable'}))\n",
    "coefs = coefs[~coefs['variable'].str.startswith('date') & (coefs.variable != 'const')]\n",
    "\n",
    "coefs.plot(x='variable', y='coef', kind='bar',\n",
    "           ax=ax, color='none', capsize=3,\n",
    "           yerr='error', legend=False, rot=0, title='Impact of Architecture and Training Parameters on Out-of-Sample Performance')\n",
    "ax.set_ylabel('IC')\n",
    "ax.set_xlabel('')\n",
    "ax.scatter(x=pd.np.arange(len(coefs)), marker='_', s=120, y=coefs['coef'], color='black')\n",
    "ax.axhline(y=0, linestyle='--', color='black', linewidth=1)\n",
    "ax.xaxis.set_ticks_position('none')\n",
    "\n",
    "ax.annotate('Batch Size', xy=(.02, -0.1), xytext=(.02, -0.2),\n",
    "            xycoords='axes fraction',\n",
    "            textcoords='axes fraction',\n",
    "            fontsize=11, ha='center', va='bottom',\n",
    "            bbox=dict(boxstyle='square', fc='white', ec='black'),\n",
    "            arrowprops=dict(arrowstyle='-[, widthB=1.3, lengthB=0.8', lw=1.0, color='black'))\n",
    "\n",
    "ax.annotate('Layers', xy=(.1, -0.1), xytext=(.1, -0.2),\n",
    "            xycoords='axes fraction',\n",
    "            textcoords='axes fraction',\n",
    "            fontsize=11, ha='center', va='bottom',\n",
    "            bbox=dict(boxstyle='square', fc='white', ec='black'),\n",
    "            arrowprops=dict(arrowstyle='-[, widthB=4.8, lengthB=0.8', lw=1.0, color='black'))\n",
    "\n",
    "ax.annotate('Dropout', xy=(.2, -0.1), xytext=(.2, -0.2),\n",
    "            xycoords='axes fraction',\n",
    "            textcoords='axes fraction',\n",
    "            fontsize=11, ha='center', va='bottom',\n",
    "            bbox=dict(boxstyle='square', fc='white', ec='black'),\n",
    "            arrowprops=dict(arrowstyle='-[, widthB=2.8, lengthB=0.8', lw=1.0, color='black'))\n",
    "\n",
    "ax.annotate('Epochs', xy=(.62, -0.1), xytext=(.62, -0.2),\n",
    "            xycoords='axes fraction',\n",
    "            textcoords='axes fraction',\n",
    "            fontsize=11, ha='center', va='bottom',\n",
    "            bbox=dict(boxstyle='square', fc='white', ec='black'),\n",
    "            arrowprops=dict(arrowstyle='-[, widthB=30.5, lengthB=1.0', lw=1.0, color='black'))\n",
    "\n",
    "sns.despine()\n",
    "fig.tight_layout()\n",
    "fig.savefig(results_path / 'ols_coef', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T12:17:13.754976Z",
     "start_time": "2020-06-22T12:17:13.751355Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_best_params(n=5):\n",
    "    \"\"\"Get the best parameters across all folds by daily median IC\"\"\"\n",
    "    params = ['dense_layers', 'activation', 'dropout', 'batch_size']\n",
    "    ic = pd.read_hdf(results_path / 'scores.h5', 'ic_by_day').drop('fold', axis=1)\n",
    "    dates = sorted(ic.index.unique())\n",
    "    train_period = 24 * 21\n",
    "    train_dates = dates[:train_period]\n",
    "    ic = ic.loc[train_dates]\n",
    "    return (ic.groupby(params)\n",
    "            .median()\n",
    "            .stack()\n",
    "            .to_frame('ic')\n",
    "            .reset_index()\n",
    "            .rename(columns={'level_4': 'epoch'})\n",
    "            .nlargest(n=n, columns='ic')\n",
    "            .drop('ic', axis=1)\n",
    "            .to_dict('records'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T12:17:14.947986Z",
     "start_time": "2020-06-22T12:17:14.927731Z"
    }
   },
   "outputs": [],
   "source": [
    "def generate_predictions(dense_layers, activation, dropout, batch_size, epoch):\n",
    "    data = pd.read_hdf('../12_gradient_boosting_machines/data.h5', 'model_data').dropna()\n",
    "    outcomes = data.filter(like='fwd').columns.tolist()\n",
    "    X_cv = data.loc[idx[:, :'2017'], :].drop(outcomes, axis=1)\n",
    "    input_dim = X_cv.shape[1]\n",
    "    y_cv = data.loc[idx[:, :'2017'], 'r01_fwd']\n",
    "\n",
    "    scaler = StandardScaler()\n",
    "    predictions = []\n",
    "    \n",
    "    do = '0' if str(dropout) == '0.0' else str(dropout)\n",
    "    checkpoint_dir = checkpoint_path / dense_layers / activation / do / str(batch_size)\n",
    "        \n",
    "    for fold, (train_idx, test_idx) in enumerate(cv.split(X_cv)):\n",
    "        x_train, y_train, x_val, y_val = get_train_valid_data(X_cv, y_cv, train_idx, test_idx)\n",
    "        x_val = scaler.fit(x_train).transform(x_val)\n",
    "        model = make_model(make_tuple(dense_layers), activation, dropout)\n",
    "        status = model.load_weights((checkpoint_dir / f'ckpt_{fold}_{epoch}').as_posix())\n",
    "        status.expect_partial()\n",
    "        predictions.append(pd.Series(model.predict(x_val).squeeze(), index=y_val.index))\n",
    "    return pd.concat(predictions)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T12:18:23.019712Z",
     "start_time": "2020-06-22T12:17:15.531202Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 748960 entries, ('A', Timestamp('2017-08-30 00:00:00')) to ('ZION', Timestamp('2015-03-02 00:00:00'))\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   0       748960 non-null  float32\n",
      " 1   1       748960 non-null  float32\n",
      " 2   2       748960 non-null  float32\n",
      " 3   3       748960 non-null  float32\n",
      " 4   4       748960 non-null  float32\n",
      "dtypes: float32(5)\n",
      "memory usage: 17.2+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "best_params = get_best_params()\n",
    "predictions = []\n",
    "for i, params in enumerate(best_params):\n",
    "    predictions.append(generate_predictions(**params).to_frame(i))\n",
    "\n",
    "predictions = pd.concat(predictions, axis=1)\n",
    "print(predictions.info())\n",
    "predictions.to_hdf(results_path / 'test_preds.h5', 'predictions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to further improve the results\n",
    "\n",
    "The relatively simple architecture yields some promising results. To further improve performance, you can\n",
    "- First and foremost, add new features and more data to the model\n",
    "- Expand the set of architectures to explore, including more or wider layers\n",
    "- Inspect the training progress and train for more epochs if the validation error continued to improve at 50 epochs\n",
    "\n",
    "Finally, you can use more sophisticated architectures, including Recurrent Neural Networks (RNN) and Convolutional Neural Networks that are well suited to sequential data, whereas vanilla feedforward NNs are not designed to capture the ordered nature of the features.\n"
   ]
  }
 ],
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